{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting relative permeability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an example of calculating relative permeability in OpenPNM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-24T11:28:39.821013Z",
     "iopub.status.busy": "2021-06-24T11:28:39.819398Z",
     "iopub.status.idle": "2021-06-24T11:28:40.623305Z",
     "shell.execute_reply": "2021-06-24T11:28:40.621820Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/amin/Code/OpenPNM/openpnm/algorithms/_invasion_percolation.py:358: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
      "  def _find_trapped_pores(inv_seq, indices, indptr, outlets):  # pragma: no cover\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import openpnm as op\n",
    "import matplotlib.pyplot as plt\n",
    "op.visualization.set_mpl_style()\n",
    "np.random.seed(10)\n",
    "%matplotlib inline\n",
    "np.set_printoptions(precision=5)\n",
    "op.Workspace().settings.loglevel = 40"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create network and phases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we create a network and assign phases and properties in a similar way that we used to do for the other examples. Note that `op.models.collections.phase` is added to assign fluid properties such as viscosity, surface tension, etc, and `op.models.collections.physics` is added to assign pore-scale models such as entry pressure, conduit conductance. Surface tension info will be used in Invasion percolation in the next step. Here, we assumed a user-defined value for air surface tension and contact angle, as they are not included in `collections.phase.air`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-24T11:28:40.631604Z",
     "iopub.status.busy": "2021-06-24T11:28:40.630144Z",
     "iopub.status.idle": "2021-06-24T11:28:40.791357Z",
     "shell.execute_reply": "2021-06-24T11:28:40.790027Z"
    }
   },
   "outputs": [],
   "source": [
    "pn = op.network.Cubic(shape=[15, 15, 15], spacing=1e-6)\n",
    "pn.add_model_collection(op.models.collections.geometry.spheres_and_cylinders)\n",
    "pn.regenerate_models()\n",
    "air = op.phase.Air(network=pn, name='air')\n",
    "air['pore.surface_tension'] = 0.072\n",
    "air['pore.contact_angle'] = 180.0\n",
    "air.add_model_collection(op.models.collections.phase.air)\n",
    "air.add_model_collection(op.models.collections.physics.basic)\n",
    "air.regenerate_models()\n",
    "water = op.phase.Water(network=pn, name='water')\n",
    "water.add_model_collection(op.models.collections.phase.water)\n",
    "water.add_model_collection(op.models.collections.physics.basic)\n",
    "water.regenerate_models()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply invasion percolation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to apply an invasion percolation algorithm to obtain the invasion sequence. Assuming a drainage process, the air(invading/non-wetting phase) will be invading the medium. The IP algorithm can be implemented through a user-defined inlet face. Here, we use the left surface pores in the x direction. By updating the air phase, the invasion sequence can then be found using the phase occupancy which is a property of the phase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-24T11:28:40.800227Z",
     "iopub.status.busy": "2021-06-24T11:28:40.798585Z",
     "iopub.status.idle": "2021-06-24T11:28:42.457986Z",
     "shell.execute_reply": "2021-06-24T11:28:42.456714Z"
    }
   },
   "outputs": [],
   "source": [
    "ip = op.algorithms.InvasionPercolation(network=pn, phase=air)\n",
    "Finlets_init = pn.pores('left')\n",
    "Finlets = ([Finlets_init[x] for x in range(0, len(Finlets_init), 2)])\n",
    "ip.set_inlet_BC(pores=Finlets)\n",
    "ip.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Occupancy and permeability functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To update the occupancy of phases at each saturation point, we created a custom function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sat_occ_update(network, nwp, wp, ip, i):\n",
    "    r\"\"\"\n",
    "        Calculates the saturation of each phase using the invasion\n",
    "        sequence from either invasion percolation.\n",
    "        Parameters\n",
    "        ----------\n",
    "        network: network\n",
    "        nwp : phase\n",
    "            non-wetting phase\n",
    "        wp : phase\n",
    "            wetting phase\n",
    "        ip : IP\n",
    "            invasion percolation (ran before calling this function)\n",
    "        i: int\n",
    "            The invasion_sequence limit for masking pores/throats that\n",
    "            have already been invaded within this limit range. The\n",
    "            saturation is found by adding the volume of pores and thorats\n",
    "            that meet this sequence limit divided by the bulk volume.\n",
    "    \"\"\"\n",
    "    pore_mask = ip['pore.invasion_sequence'] < i\n",
    "    throat_mask = ip['throat.invasion_sequence'] < i\n",
    "    sat_p = np.sum(network['pore.volume'][pore_mask])\n",
    "    sat_t = np.sum(network['throat.volume'][throat_mask])\n",
    "    sat1 = sat_p + sat_t\n",
    "    bulk = network['pore.volume'].sum() + network['throat.volume'].sum()\n",
    "    sat = sat1/bulk\n",
    "    nwp['pore.occupancy'] = pore_mask\n",
    "    nwp['throat.occupancy'] = throat_mask\n",
    "    wp['throat.occupancy'] = 1-throat_mask\n",
    "    wp['pore.occupancy'] = 1-pore_mask\n",
    "    return sat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As relative permeability is the ratio of effective over absolute permeability, given the same boundary condition and viscosity, the geometrical parameters in $K_{rphase}$ will cancel out. The remaining fraction includes flow rates as follows:\n",
    "\n",
    "$$\n",
    "K_{rphase}=\\frac {K_{ephase}}{K_{abs}}=\\frac {Q_{e-phase}}{Q_{abs-phase}}\n",
    "$$\n",
    "\n",
    "Where $Q_{ephase}$ is the inlet flow rate of the phase (water/air) for a Stokes flow algorithm with `multiphase conduit_conductance` assigned as conduit hydraulic conductance model to account for the existence of the other phase. Whereas $Q_{abs}$ is the inlet flow rate of the phase (water/air) for a Stokes flow algorithm with `generic_hydraulic_conductance` assigned as hydraulic conductance model for a single phase flow.\n",
    "\n",
    "\n",
    "Note that $K_{abs}$ is a property of the porous material, not the fluid. However, here to cancel out viscosity in the $K_{rphase}$ fraction, the same phase must be used to calculate $Q_{abs-phase}$. Therefore, naming `abs-phase` was used here to indicate the flow rate from a single phase flow algorithm for the same phase that relative permeability is being calculated.\n",
    "Therefore, we only need to find the rate values for calculating the relative permeability. \n",
    "\n",
    "\n",
    "We define a function `Rate_calc`, which simulates a stokes flow given the hydraulic conductance keyword and returns the rate of inlet fluid flow. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Rate_calc(network, phase, inlet, outlet, conductance):\n",
    "    phase.regenerate_models()\n",
    "    St_p = op.algorithms.StokesFlow(network=network, phase=phase)\n",
    "    St_p.settings._update({'conductance': conductance})\n",
    "    St_p.set_value_BC(pores=inlet, values=1)\n",
    "    St_p.set_value_BC(pores=outlet, values=0)\n",
    "    St_p.run()\n",
    "    val = np.abs(St_p.rate(pores=inlet, mode='group'))\n",
    "    return val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example we are focusing on finding the relative permeabilities in x direction. A similar procedure can be applied on y and z directions. Let's define inlet and outlet pores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "flow_in = pn.pores('left')\n",
    "flow_out = pn.pores('right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define multiphase conductance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assigning multiphase conductance models to the phases. The multiphase conduit conductance accounts for the presence of the other phase in effective permeability calculations. For more details, please see the model's code in OpenPNM package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_mp_cond = op.models.physics.multiphase.conduit_conductance\n",
    "air.add_model(model=model_mp_cond, propname='throat.conduit_hydraulic_conductance',\n",
    "              throat_conductance='throat.hydraulic_conductance', mode='medium', regen_mode='deferred')\n",
    "water.add_model(model=model_mp_cond, propname='throat.conduit_hydraulic_conductance',\n",
    "              throat_conductance='throat.hydraulic_conductance', mode='medium', regen_mode='deferred')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate relative permeabilities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have defined the required models and functions, we can find the relative permeability of phases over a range of saturation. We selected 10 saturation points. A higher value can be used to obtain a better approximation of the curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Snwp_num = 10\n",
    "flow_in = pn.pores('left')\n",
    "flow_out = pn.pores('right')\n",
    "max_seq = np.max([np.max(ip['pore.invasion_sequence']),\n",
    "          np.max(ip['throat.invasion_sequence'])])\n",
    "start = max_seq//Snwp_num\n",
    "stop = max_seq\n",
    "step = max_seq//Snwp_num\n",
    "Snwparr = []\n",
    "relperm_nwp = []\n",
    "relperm_wp = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(start, stop, step):\n",
    "    air.regenerate_models()\n",
    "    water.regenerate_models()\n",
    "    sat = sat_occ_update(network=pn, nwp=air, wp=water, ip=ip, i=i)\n",
    "    Snwparr.append(sat)\n",
    "    Rate_abs_nwp = Rate_calc(pn, air, flow_in, flow_out, conductance='throat.hydraulic_conductance')\n",
    "    Rate_abs_wp = Rate_calc(pn, water, flow_in, flow_out, conductance='throat.hydraulic_conductance')\n",
    "    Rate_enwp = Rate_calc(pn, air, flow_in, flow_out, conductance='throat.conduit_hydraulic_conductance')\n",
    "    Rate_ewp = Rate_calc(pn, water, flow_in, flow_out, conductance='throat.conduit_hydraulic_conductance')\n",
    "    relperm_nwp.append(Rate_enwp/Rate_abs_nwp)\n",
    "    relperm_wp.append(Rate_ewp/Rate_abs_wp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the relative permeability curves:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd055caf220>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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9ZzT9gU7qUD8o13mbo85rxFeRSs+ymsea1wzQK7c1V3jDYJflXxpRTSikffv26dixY3m2JyUlaefOnbmOp6cXfshb69attWzZMo0YMUJHjx5VRkaGlixZkiuuYcOGmj59ukJDQwt9zfLEXgFpbNcGJZRN6bVw4UINGTJEaWnZQ0SbNGmilStXFqpACgAAAACFdT4pXev+OqeTF5JtjqdnWTVz0wm7BaQfNh03i0dV/Lz1zI3Xa0i7unans5U3TGEr4zp37qxdu3bp008/Vc+ePVWzZk15eXmpZs2a6tmzpz799FPt3LlT4eHhJZ1qmWK1Goo8et7mWOTR86yDdIWff/5ZgwYNMotHLVu21Nq1ax0Wj1avXm1O04yIiDCPL1q0SMOGDdN1110nPz8/WSwWffTRR0Wa75gxY8xrT5kyRVJ2kfezzz5Tt27dVL16dXl7e6tu3boaNmyY1q1bl2dfmZmZ8vf3l8Vikbe3t5KTk/OM7dq1q3nd/GJ79uxpxl55/byeu7lz52rAgAGqX7++fHx8VKNGDd1www2aMmWKrFarAAAAgPLkTEKq3l60X93eWamPV/6lxLSsXDGe7vYLQp5uf5dJnrupiYZ2qEfx6P8xAqmQoqKiiqyv+vXryzCuvkDh5+en8ePH2yysjcI5EJNgc1tHSbqYnKGDsQlMY/t/M2bM0KhRo5SVlf3LuG3btlq2bJmCg69uWGd8fLzGjh2rOXPmFEeaDu3fv1+DBw/W/v37bY6fPHlSM2fO1MyZM/XKK6/o9ddfz3Wuh4eHunbtqqVLlyo9PV3r169X3759c8UlJydr8+bN5r6j2NTUVEVGRkqSfH191bFjR4f5JyQkaNSoUZo7d67N8djYWMXGxmr58uX67LPPNHfuXEaEAQAA4Jp36mKKvlxzWD9sPqH0TMdfpA5pV0eZWVZ9/cdR/RmboOur++v+bg00pH0dzdpyQpI0b8dp3dm+ritSLxMoIAF25Jy+1qBKRR09l2Qep4AkTZ48Wffff785uiU8PFyLFy9WYGDgVfVjGIZGjhyphQsXymKxqEOHDmrWrJkMw9CePXtksRRfpf/06dPq27evTp8+rcDAQHXv3l01atTQuXPntHLlSsXHx0uS3njjDTVv3lxDhw7N1UdERISWLl0qKXsRcXtFoXXr1plrkV2WV+yGDRvM0VxdunSRp6enw8cwduxYs3jUsWNHtWjRQmlpadq4caOOHDkiSdq8ebN69+6t9evXKygo9xBdAAAAoKw7Fpekz1cf1s/bTiojK/9BGfWDfVU9wEdDv9yorccumMeX74vVhKFhCgn21bG4ZK07fE6nLqaodmCF4ky/zKCAhHLpzYX79Ouu6Dzbc44+eqR3Yz05O3sdq/eWHtQXa47ked6trWrqpVubF12ipdCkSZM0fvx4c7Rcjx49tHDhQvn7+191X+vXr1dmZqZatmyp6dOnq2XLljbtl4spxeGNN95QWlqann32Wb366qvy9fU1286fP68777xTK1eulCS9+OKLuuuuu3IVtHJOI1u1apXd6+S8m2LVqlV19uzZPGNzHs/Ztz0bNmxQenq6GjRooJkzZ+YarTR16lSNGzdOaWlpOnjwoJ544gl99913DvsEAAAAypJDsQn6dNVfmr/ztK5mtZGACp7qP/F3JaRl2hzfcuyC+k/8XfWrVJQkGYY0Z9tJPdz7uqJMu8yigIRS5/mfd+lgbEKx9W8YhvZHJygtnyGNkhRc0Uu3t66lt37dr7ikdCWnZyk5Pff82cumbTymrccv5NleFJpU99d/B7cq1mvkZfbs2TbTvfr27at58+bZFF+uRmZmpmrUqKGVK1eqSpUqudq9vb0LnGt+0tLS9MILL+jtt9/O1RYUFKQZM2aoUaNGSkpK0pEjR7Rp0yZ16tTJJq59+/by9/dXQkKCtmzZoqSkJFWsWNEm5nIBqWHDhurXr58mTZqUb6wk9erVy2H+6enpqlixon777Tc1atQoV/uoUaPk7u6ukSNHSsouKD333HNq3vzaLnACAACg9LNaDQ38bJ2S07Pk6+WuueO7yu0q1hnacypen676S0v2xsjZVWAqeLorJSP7s9yuk/Hm8bpBFfRwr8b6ZNVfOnE+RQlpmdp96u/2n7ae1EO9Ghfr7IiyggISSp2DsQnafvyiy6/r7mZRVb+/CxbVArz14V2t5eHuplkPhuvJ2Tt15tLfI2LOJqYp64oyd2qmtURyd5WcxaPQ0FAtWLBAPj4+herzlVdesVs8Km5Vq1bVK6+8kmd79erVdcstt2j27NmSsqeCXVlAurwO0pIlS5SRkaE//vhDN954o9mec/2jiIgI9erVS5MmTbIbm5KSYrP+UYcOHfJ9DE899ZTd4tFlI0aM0Oeff24uxv3VV19pwoQJ+fYLAAAAFKcDMQk2RRxn15rdeuyCPll5SKsOnnXqOhaL1D+0pno2qaJnf9qdq31Qm9p6fUAL+ft4qn/Lmnpl3l7N2X7KJiYqLllbj11Qezt3bCtvuAsb8P+yrIba1AvU7Ac7a+OLfTT/4W5qXC17Wlbjav6a/3A3bXyxj2Y/2FlhdQNzFY/Km7179+r7778vVB8Wi8Xu2kKucNttt+Vb/GrTpo25ndeC+Y6msf3xxx/m+ke9evVyGLt+/Xqlp6dLyr5rW37rH0nZo4zyM3r06DyvCQAAAJSEnGvO2tvPyTAMrT98TsO/2qjBn693qnjk7mbRoLa19dsTPfTpiLbaGnXRpt3f20MT7w7Th0PD5O+T/e9ufx9PTRgapol3h8nf23aszU9bTzr5yK5tFJCAHBbviVHfD9doc9R5u+2bo86r74drtGRvjIszKx3uv/9+NWzYUFL2L/IHH3xQU6dOLXB/9evXL7GFna9cb8menHeUu7yo9pUcFYVyTkmLiIhQtWrVzClkV8ZezfpHklSlShWHo48u69y5s7m9Z8+eXAt6AwAAAK7mTAHJMAytOnhGQyZt0PCvIrX+cN5Fpss83S0a1rGeVj0VoQ/vCjMHBAwIq6Uqfl6SpPYhlbXose4aEFbbbh8Dwmpr0WPd1T6ksiSpil/2siZgChuQS3qWVTM3nVAHO0MUf9h0XOlZ+a+ddK2qXbu2Vq5cqR49euj48eMyDEP33nuvvL29CzSSqGrVqsWQpXMqVaqUb0zOUUB5FV5yroO0detWJSQkmAuKXy4gNW7cWHXq1JGUXRzat29fnrGXY/JTr169fGMkqW7dv287mpWVpQsXLqhatWpOnQsAAAAUNavVUORR2y/sI4+el9VqyM3NIqvV0LJ9sfpk1SHtOXXJqT69Pdw0rGM9PdizoWpWyn3HtC6Nq+jXR7tryZ4YjehUTx7ujsfS1A3y1cxx4ZoeeVw3hdZQ9YDCLdtxraCAhFKnSfWrv5tXUYhLTNPx8ymSsivX9ni6/f2Lpl5QBQX7Fd8iz3kpqefnspCQELOIdPr0aWVlZWnkyJHy9vbWwIEDr6qvChVK7naYRbUInru7u7p166bFixcrKytLv//+u/r376+kpCRt2bJFku2C2BEREfrss89sYpOTk7Vp0yZJUsWKFZ1a/8jZhcuvXKg7ISGBAhIAAABKzIGYBJu7XkvSxeQM7YuO1+GzSfp01V/6MzbRqb4qerlrZOcQ3d+toar6O/5sVj3AR6O71Hc6Tw93t6uKLw8oIKHUKak7jI34eqNZQBrSro4ys6z6+o+j+jM2QddX99f93RpoSPs6mrXlhCSpXlBFTbu/k6Mur1mNGjXSypUr1bNnT8XGxiozM1NDhw7VnDlz1L9//5JOz+UiIiK0ePFiSdlT0fr3759r/aOcsRaLJXtI7v/Hrlu3zox1dv2j5ORkp3JLSkqy2b884gkAAAAoCTmnqzWoUlFHz2X/e3XUN5t0Ptm55RYCfDw0tmsDje1aX4G+XsWSJ3KjgARIOnkh2ZxTWz/YV9UDfDT0y43aeuyCGbN8X6wmDA1TSLCvjsUla93hczp1MUW1A0tuFE1JatKkiZYvX66IiAjFxcUpPT1dgwcP1oIFC9S3b9+STs+l7K2DlNeUtKpVq6p58+bau3evGXu16x9J0vHjx686zt3dXZUrV3bqPAAAAKAg3ly4T7/uis6zPefoo0d6N9aTs3dKklPFo6CKXrq/ewPdEx5iLn4N12ERbUDSnG2nZPz/TdUCKniq/8TfbYpHkrTl2AX1n/i7Av7/F5VhSHO2le/V+ENDQ/Xbb78pMDBQkpSamqoBAwZo7dq1JZuYi7Vr184c2bN9+3bFx8ebBaQmTZqoZs2aNvGXRyRdGSs5X0A6d+6c/vrrr3zjNm7caG6HhoY6NboJAAAAKAjDMLRkT4xiLqXm+ZOSkSVJCq6YvTh1cEXnRhBV9vXUH8/10viIxhSPSggFJJR7hmHopxyFoF0n45WQlilJqhtUQe8Mbqm6QdmjjBLSMrX71N934/pp60kZlytP5VSbNm20dOlSs4CSnJysW265xaZwca27vA6SJFmtVi1atMju+keXXS4SXRnr7PpHl33//ff5xnz33Xfmtr1cAAAAgKJyITlDZxPSch13d7OoRoCP+dOqTiXNejBcHu5umvVguFrVqWTT7u6We73SpLQspWaU3xsalQYUkFDubTl2Qcficq8nM6hNbS16tLuGdqinRY921x1tct/mMSouOddIpfKoY8eOWrRokblgc2Jiom666SZt3bq1hDNznZzFmbfffluZmZm5jl92eR2ky7GX1z/q1q2bPDycn1n8wQcf6PDhw3m2T58+XevWrTP377//fqf7BgAAAK5WUEUvLX+yp24OrWFzPMtqqE29QM1+sLM2vthH8x/upsbVsr+AblzNX/Mf7qaNL/bR7Ac7K6xuoLKstl/S929ZQyue6qkgJ0croXhQQEK599MW22lo/t4emnh3mD4cGmYOjfT38dSEoWGaeHeY/L1tP+D/tLV8T2O7rFu3blqwYIF5Z7X4+Hj169dPu3btKuHMXCPn1LM9e/bYPX5ZcHCwQkNDnYrNi5eXl5KSknTDDTdo8+bNudq///573Xfffeb+yJEj1aJFC6f7BwAAAAqiXrCvPh/ZTjPHhat5zQDz+OI9Mer74Rptjjpv97zNUefV98M1WrI3xjzWvGaAZo4L12cj2qlukHN3IUbxoYCEcm9AWC1V8cuuZLcPqaxFj3XXgLDco42yY2tr0WPd1T4keyHiKn7Z83aRrVevXpozZ468vbNvoXn+/Hn17dtX+/fvL+HMil/btm0VEBBgc6x58+aqVq2a3XhHU9uc0blzZw0aNEhHjx5Vp06dFB4ervvuu0/33HOPGjdurFGjRiktLXv48HXXXaePPvrI6b4BAACAwqrg6S73KyoO6VlWzdx0wm78D5uOKz0re4paFT9vvTO4pRY80k3hDYOLO1U4ibuwodzr0riKfn20u5bsidGITvXkceVvuSvUDfLVzHHhmh55XDeF1lD1AB8XZVo23HjjjZo9e7aGDBmijIwMnT17Vn369NGaNWt03XXXlXR6xebyOkiLFi0yjzlacygiIkL/+9//zH0/Pz+1b9/+qq45ZcoUZWZmav78+YqMjFRkZGSumHbt2mnevHkKDuYPLwAAAIrfhaR0vbv0oGZuPi57y8V6uude30iSPN3+/hz23E1NdGf7usWVIgqIEUiApOoBPhrdpX6+xaPLPNzdNLpLfYpHebj99ts1Y8YMubu7S5Kio6PVp08fHT16tIQzK15XFowcjSjq2bOnuQ6SJHXt2vWq1j+SJH9/f82dO1c//vijbr31VtWtW1deXl6qWrWqevfurW+++UaRkZGqXdv+iDoAAACgqFithn7YdFy9PlitHzbZLx5J0pB2dZSZZdWkNYf15OwdmrTmsDKzrBrSvo4ZM2/HaRdljathMcr7LaSQp86dO+e6k1Z4eLg2bNhQQhkB5dvq1avNIlXPnj21evXqkk0IAAAAkLTr5EW9PG+vdp646DCufrCvvr+vkx6ftcPmZkTtQyprwtAwjfwmUsfikmWxSH8811u1AysUc+ZlT0l+TmcKGwAAAAAAuGoXk9P13tKDmuFgxFFOARU81X/i70pIy7Q5vuXYBfWf+LvqV8m+q7NhSHO2ndTDva/dJTDKIqawAQAAAAAAp1mthmZuOq5e76/W9EjHxaOcKx7tOhlvFo/qBlXQO4Nbqm5Q9iijhLRM7T4Vb8b+tPWkmDBVulBAAgAAAAAATtl9Ml6DPl+v53/ZrQvJGQ5jO9SvLHsloEFtamvRo901tEM9LXq0u+5ok3vNzqi4ZJtpbih5FJAAAAAAAIBDF5PT9dLc3br90z+0I5+1jkKCfTV5TAc1rOJnc9zf20MT7w7Th0PD5O/jmX3Mx1MThoZp4t1h8ve2XWXnp60ni/QxoHBYAwlAqTVx4kQdOnSoUH2Eh4dr5MiRRZQRAAAAUL5YrYZ+2npS/11yQOeT0h3Genu46aFejTWuR0P5eLrL28NNKw7E6lxiurlQdt0gX7vnDgirrbb1KuuJWTu05dgFVfHz0u2taxXHQ0IBUUACUGrNmTNHa9asKVQfiYmJFJAAAACAAthzKl4vz9uj7ccv5hvbt1l1vXpbc5sCUZfGVfTro921ZE+MRnSqJw93x5Og6gb5aua4cE2PPK6bQmuoeoBPYR8CihAFJAAoIyIiIlhIEAAAAMUuPjlDH/x2UNM2HpM1n39+1gvy1au3NVefZtXttlcP8NHoLvWdvraHu9tVxcN1KCABKLVWr15d0ikAAAAA5YbVaujnbSf138UHFOfEdLXxEY31YM/s6Wq49lFAAgAAAACgnNt7Ol6vzNvr1J3P+jarpldubaF6wfbXM8K1iQISAAAAAADlVHxKhj5cdlDfOzFdrW5QBb16awv1bW5/uhqubRSQAAAAAAAo46xWQwM/W6fk9Cz5erlr7viucnOz5BlvGIZ+3nZK/128X+cSHU9X8/Jw0z97NtI/IxoxXa0co4AEAAAAAEAZdyAmQbtOxpv7B2MT1KxmgN3Yfacv6dX5e7Q5Kv/par2aVNVrt7dQSHDFIssVZRMFJAAAAAAAyriNR+Jy7V9ZQLqUmqEPl/2pqRui8p2uVqdyBb16Wwv1bVZNFkveI5lQflBAAgAAAACgjLNXQBrbtYGk7Olqc7af0tuLDuhcYprDfrw83PSPno00nulquAIFJAAAAAAAyjCr1VDk0fM2xyKPnpfVaujPMwl6ea5z09UimlTVa7e1UP0qTFdDbhSQAAAAAAAoww7EJCg+JcPm2MXkDD05e6cW7DqtrHzmq9UOrKBXbmuufs2rM10NeaKABAAAAABAGZZz+lqDKhV19FySJGnujlMOz/Nyd9ODPRtqfERjVfBiuhoco4AEAAAAAEAp9ubCffp1V3Se7TlHHz3Su7GenL0z3z69PNw0oHUtPdWvSZHkiGsfBSQAAAAAAEopwzC0ZE+MYi6l5hsbXNFLt7eupbd+3a+4pHSHsemZVm08GucwBsiJAhIAAAAAAKXUheQMnU3Ifec0dzeLqvp5m/vVArz14V2t5eHuplkPhuvJ2Tt15tLf551NTMu1FlJsfJrOJ6UrqKJX8T0AXDMoIAEAAAAAUEoFVfTS8id76j+L92vxnhjzeJbVUJt6gXrh5maqF+xrc07jav6a/3A3SdLxuGS9vWi/luyNsYnp37KGXri5GcUjOI0CEgAAAAAApVi9YF99PrKdNh6J0xsL9mlf9CVJ0uI9MVqx/4ymP9BJHeoH5Tpvc9R5jfgqUulZVvNY85oBeuW25gpvGOyy/HFtcCvpBAAAAAAAQP46NQjSyPB68nK3mMfSs6yauemE3fgfNh03i0dV/Lz1zuCWWvBIN4pHKBBGIAEAAAAAUMqdSUjVCz/v1ooDZ3K1eeYoKNkcd/t7zMhzNzXRne3rFlt+uPYxAgmAQxEREbJYLLJYLHrttdecOmfHjh2qWrWqeV6VKlW0bdu24k0UAAAAuEYt3h2tGyestVs8kqQh7eooM8uqSWsO68nZOzRpzWFlZlk1pH0dM2bejtOuShfXKEYgAShSmzdv1o033qgLFy5IkqpVq6bly5erZcuWJZwZAAAAULbEp2To9fl79cv2U3nG1A/2VfUAHw39cqO2HrtgHl++L1YThoYpJNhXx+KSte7wOZ26mKLagRVckTquQRSQgJwy06WDv0p/LZdO75TiDkmZaZKHtxR8nVSrtdS4r9TkFsmDuxVcad26derfv78uXcpe1K9WrVpasWKFmjZtWsKZAQAAAGXLH4fO6Zmfdio6PtVhXEAFT/Wf+LsS0jJtjm85dkH9J/6u+lUqSpIMQ5qz7aQe7n1dseWMaxsFJECSsjKkDZ9m/yTZGRaamSrF7s7+2T5N8qsuhY+XOj8kuXu6Pt9SaNWqVbrtttuUlJQkSapXr55WrlypRo0alXBmAAAAQNmRkp6ld5Yc0JT1UU7F7zoZb27XDaqgh3s11ier/tKJ8ylKSMvU7lN/t/+09aQe6tVYFov9NZMARyggAWf2S3MelKJ32h63uEvBjSVvPyktUYr7SzKystsSY6Xlr0p7f5Hu+EKq1sz1eZciy5Yt08CBA5WSkiJJatSokVasWKGQkJASzgwAAAAoO3aeuKgnZu/QkbNJV33uoDa19fqAFvL38VT/ljX1yry9mnPF1LeouGRtPXZB7esHFVXKKEcoIKF8Ox4pTR8ipWVPuZLFTWp6q9R+rFQ3XPLy/Ts2PVk6sVHaMlk6sFAyrNlFp2/6SSN+kup1KpnHUMIWLlyoIUOGKC0tTZLUpEkTrVy5UrVq1SrhzAAAAICyISPLqo9X/qVPV/2lLKuRZ5y/j4ea1wxQ5NHzfx/z9tC/7wjVgLDaOeI8NWFomCKaVNVLc/bYTG/7aetJCkgoEO7ChvLrzH7b4lHVptL9y6Wh30uNetsWj6Ts/Ua9s9vvX54dL2WfP32IdOaAa/MvBX7++WcNGjTILB61bNlSa9eudVg8Wr16tXl3toiICPP4okWLNGzYMF133XXy8/OTxWLRRx99VCR5xsTEmNesW9fxrUvr1KnjdGz9+vXN2FOnbL/dmTJlitk2ZswYSVJmZqa+//579evXT3Xq1JG3t7dq166tAQMG6JdffinUYwQAAEDZ9NeZBA36bL3+t+KQw+JR18bBWvp4Dz3W5zpV8ctej7V9SGUteqy7TfEopwFhtbXose5qH1JZklTFz0u3t+aLXhQMI5BQPmVlZE9bu1w8ahgh3T1D8qro3Pm120kPrJRmDpeOrM7uZ8446f4V5WZNpBkzZmjUqFHKysqe1te2bVstW7ZMwcHBV9VPfHy8xo4dqzlz5hRHmpKkGjVqqEmTJjp48KBOnjypv/76S40bN84Vd+jQIZtCkKPYo0eP6tixY5Kkxo0bq3Zt+3+0L4uJidGdd96pP/74w+b46dOnNX/+fM2fP18333yzZs2aJX9//4I8TAAAAJQhVquhKeuj9M6SA0rLtOYZ5+3hphdubqpRnevLzc2iWoEV9Ouj3bVkT4xGdKonD3fH40LqBvlq5rhwTY88rptCa6h6gE9RPxSUExSQUD5t+PTvNY+qNr264tFlXhWzz/uqt3T2QHZ/Gz6Vuj1e5OmWNpMnT9b9998vqzX7D114eLgWL16swMDAq+rHMAyNHDlSCxculMViUYcOHdSsWTMZhqE9e/YU6eJ+EREROnjwoKTsBb/tFYVWr16d65gzsTlHUtmTkZGhgQMHKjIyUu7u7uratauuu+46JSYmau3atYqOjpYkLV68WDfffLNWrlwpLy/u8gcAAHCtOnUxRU/P3qkNR+IcxrWuU0kf3BWmxtX8bI5XD/DR6C71nb6eh7vbVcUD9lBAQvmTmS5t/Cx72+ImDfzs6otHl3lVlAZ8Jn3TN3tNpI2fXfN3Zps0aZLGjx8vw8geXtujRw8tXLiwQKNm1q9fr8zMTLVs2VLTp09Xy5YtbdovT40rChEREfriiy8kZReFHnjggVwxl4tCPj4+8vDwUGJiYp6xq1atsunbkZ9++knp6elq06aNZs6cqeuvv95ss1qtev/99/X888/LMAytW7dOb731ll5//fUCPEoAAACUZoZh6Jdtp/Ta/L026xJdycPNokd6X6eHejXKd4QR4Cq8ElH+HPw1+y5qUvaC2bXbFa6/Ou2kprdkbyfGZi+wfY2aPXu2/vnPf5rFo759+2rx4sUFnnKVmZmpGjVqaOXKlbmKR5Lk7e1dqHxzylnksTfSSJLWrFkjSerSpYu6devmMPZqRiClp6erTp06+u2332yKR5Lk5uamZ599Vm+++aZ57N1339X58+ev7AYAAABlWFximv4xbaue+nGnw+JRo6oV9cv4Lnqs73UUj1CqMAIJpc/8R7IXuC4u54/+vd1+bNH02f5eaf+C7O1fn86eylZcqjWTbv+4+Pp3YP/+v/+/hIaGasGCBfLxKdwc6ldeeUVVqlQpbGr5qlGjhpo2baoDBw4oOjpaBw8eVJMmTcz2P//801z/KCIiQt7e3lqyZInd2MOHD+vEiROSpOuuuy7f9Y8k6Y033nC4PtSzzz6rr7/+WlFRUUpNTdX333+vxx57rKAPFwAAAKXIb/ti9cIvu3QuMd1h3L1dG+jZm5rIx9PdRZkBzqOAhNLnzH7p5Obiv47FXarXuWj6qhuePR3OsErJ57J/rnF79+7V999/b3d6l7MsFouGDh1ahFk5FhERoQMHsu+Wt2rVKpuiUM4RRb169bJZg8hRbH6jj6TskVR33XWXwxhPT08NHz5cb7/9tnlNCkgAAABlW0Jqht5cuE+zt5x0GFerko/ev6u1ujQq/i9WgYJiPBzKr+DGkmeFounLyze7v2vc/fffr4YNG0rKnr/94IMPaurUqQXur379+goKCiqq9PKVs9iTcw0j6e+ikK+vrzp27Kh27dopICDAbuzVrH8kSS1btlTFivmvs9W5898Fze3bt+cbDwAAgNJr45E43Tzx93yLR4Pb1tGSJ3pQPEKpRwEJ5Ze3X/4xV8OriPsrhWrXrq2VK1eqXr16krKLSPfee69mzZpVoP6qVq1alOnly9E6SDnXP/Ly8pK7u3ue6yBdjr2yz7xcfr7yU7duXXP77NmzTp0DAACA0iU1I0tv/bpPw77aqJMXUvKMC6ropUkj2+mDu1orwOfavQkPrh0UkFB+pSUWbX/pRdxfKRUSEqKVK1eqVq1akqSsrCyNHDlSc+fOveq+KlQoohFgTqpevbqaNWsmSTpz5oz27dsnKXv9o9OnT0vKnr522eXiUM7YQ4cO6eTJ7G+Rrr/+evN5cMTX19ep/HKOUkpJSVFWVpZT5wEAAKB02HMqXrd/8oe++v2o/v++M3b1bVZNSx/voZtCa7guOaCQWAMJpU+1ZsXbf+weKSNFivsr+79FMY0tPTm7P0ny9JWqtyh8n3kp7ufHCY0aNdLKlSvVs2dPxcbGKjMzU0OHDtWcOXPUv3//kk7PoYiICHMx8FWrVql58+Y2U9JyFpBybl+Ovdr1jyQpOTnZqbikpCRzu0KFCnJ3Z/FEAACAsiAzy6pJaw7ro+WHlGnNu3Lk5+2hV25rrjvb1ZHFYnFhhkDhUUBC6VPcdxib95C0fZpkZEnHN0iNehe+zxMbsxfQlqTQwdKATwrfZynXpEkTLV++XBEREYqLi1N6eroGDx6sBQsWqG/fviWdXp4iIiL0+eefS8ouCj300ENmUcjPz08dOnQwY9u0aaNKlSopPj7ejL3a9Y8k6fjx41cd54o70wEAAKDwjp5L0pOzd2j78YsO4zo2CNIHd7ZW3SDnRqcDpQ1T2FD+NM5R3NgyuWj63PJtjv77FE2fZUBoaKh+++03BQYGSpJSU1M1YMAArV27tmQTcyBn0WfNmjUyDMNc06hr167y8Pi7ru7u7q7u3bvbjb2yL0d2795tM7ooLxs3bjS327Zt61TfAAAAKBmGYej7DVHqP/F3h8UjL3c3/at/M818IJziEco0Ckgof5rcIvlVz94+sFA6tbVw/Z3cKh34NXvbr7rU9NbC9VfGtGnTRkuXLpW/v7+k7Olat9xyi00xpDSpVq2auQ7SuXPn9PPPPys6OlqS7ZS1yy4XiS7HXl4r6frrr1fNmjWdumZaWppmz57tMCYjI0MzZsww9+3lAgAAgNIhJj5Vo77dpJfn7VVKRt7rVraoFaAFj3TTAz0ays2NKWso2yggofzx8JLCx2dvG1Zp7ngpPf/RIXalJ0nzxv89fS18vORe/u6g0LFjRy1atMhcBDoxMVE33XSTtm4tZHGumOQszrz++ut2jxck1pGXX35ZcXFxeba/++67ioqKkiR5e3tr5MiRV9U/AAAAXGPejlPqN2GNfj90Ls8YN4v0cK/GmjO+q5rU8HdhdkDxoYCE8qnzQ1LN1tnbZw9IM4dffREpPSn7vLMHsvdrhkmdHy7SNMuSbt26acGCBead1eLj49WvXz/t2rWrhDPLLefUsz179kiS/P391a5du1yxYWFh5hS9y7FX9pEfLy8vnTp1Sv369dOhQ4ds2qxWq9577z29/PLL5rFnnnlGwcHBTvcPAACA4nchKV0Pz9imx2bu0KXUzDzjGlSpqJ/+2UVP39hEXh585Ma1g0W0UT65e0p3fCF9009KuyQdWS191Vsa8JlUJ3cRIZeTW7NHHl0uHnlXyu7PvXy/pXr16qU5c+ZowIABSktL0/nz59W3b1+tWbPGnDZWGtgr/nTv3t3uXc/c3NzUo0cPzZ8/P98+8jJkyBAdPnxYkZGRatasmbp3767GjRsrMTFRa9euNafFSVLnzp310ksvOd03AAAArp7VamjgZ+uUnJ4lXy93zR3f1eEUs1UHz+i5n3bpTEKaw35HdQ7R8zc3la9X+f5cgGsT5VCUX9WaSSN+krwDsvfPHpC+6SvNGikdXimlX3Hr9fTk7OOzRmbH5SwejfhRqtbUtfmXUjfeeKNmz54tT8/sqXxnz55Vnz59co28KUlVq1ZV8+bNbY45mpJ2ZbGoSZMmqlGjhtPX8/T01Jw5c9S5c2dlZWVp9erV+vrrrzVz5kyb4lG/fv20ZMkSeXt7O903AAAArt6BmATtOhmvv84katfJeB2MTbAbl5SWqX/N2a2xkzc7LB5VD/DW1Hs76o0BoRSPcM3ilY3yrV4n6b7fpDnjpOid2WsZ7V+Q/WNxk4IbS15+UnqiFPfX32sdXVaztXTHlxSPrnD77bdrxowZuvvuu5WVlaXo6Gj16dPH5g5mJa1Xr17at2+fue9oRNGVxaWrGX10Wc2aNbVmzRpNmzZN06dP1/79+3Xu3DkFBQWpffv2GjNmjAYPHnzV/QIAAODqbTwSl2u/Wc0Am2Nbj53Xk7N36ljcFV8sX2FAWC29cXuoKvmWv7VQUb5QQAKqNZXuXyFt+FTa+JmUGJt93LBK5/60f45f9ewFszs/dM0vmr169eoCnTdkyBBlZuaeG96gQQMZhlHIrArvk08+0SeffOJUbFhYWJHk7OnpqbFjx2rs2LGF7gsAAAAFZ6+ANLZrA0lSeqZVE5b/qS/WHJbVwT8BK1Xw1Ft3hOrWVrWKM1Wg1KCABEjZRaBuj2cXhA4slP5aIUXvkM79JWWmSh4+UpXG2QtlN+4jNb31mi8cAQAAANciq9VQ5NHzNscij56X1WrozzMJemLWTu2PvuSwj4gmVfXO4FaqHuBTnKkCpQoFJCAnd0+pxR3ZPwAAAACuOQdiEhSfkmFz7GJyht76dZ++33hc6VnWPM6UKni666Vbm2l4x3qyWPJedBu4FlFAAgAAAACUGzmnrzWoUlFHzyVJkr5ZF+XwvHYhlfXBna1Vv0rF4kwPKLUoIAEAAAAArhlvLtynX3dF59mec/TRI70b68nZO/Pt08/bQ2F1AykeoVyjgASg1Jo4caIOHTpUqD7Cw8M1cuTIIsoIAAAApZlhGFqyJ0Yxl1LzjQ2u6KXbW9fSW7/uV1xSusPYxLRMLdsXo5dvbV5UqQJlDgUkAKXWnDlztGbNmkL1kZiYSAEJAACgnLiQnKGzCWm5jru7WVTVz9vcrxbgrQ/vai0PdzfNejBcT87eqTOX/j7vbGKasq64BVtsfJrOJ6UrqKJX8T0AoBSjgAQAxWDMmDEaM2ZMSacBAABQrgRV9NLyJ3vqP4v3a/GeGPN4ltVQm3qBeuHmZqoX7GtzTuNq/pr/cDdJ0vG4ZL29aL+W7I2xienfsoZeuLkZxSOUaxSQAJRaq1evLukUAAAAUMbUC/bV5yPbaeOROL2xYJ/2RV+SJC3eE6MV+89o+gOd1KF+UK7zNked14ivIm3uwta8ZoBeua25whsGuyx/oLRyK+kEAAAAAAAoauENg7XgkW56/fbm8nS3SJLSs6yauemE3fgfNh03i0dV/Lz1zuCWWvBIN4pHwP9jBBIAAAAA4Jp06kKKpm08roysv9czulxMupKn29/jK567qYnubF+32PMDyhJGIAEAAAAArjkbDsdpwKd/6NCZRJvjQ9rVUWaWVZPWHNaTs3do0prDysyyakj7OmbMvB2nXZ0uUOoxAgkAAAAAcE2ZEXlcr8zbo8wr7qRWP9hX1QN8NPTLjdp67IJ5fPm+WE0YGqaQYF8di0vWusPndOpiimoHVnB16kCpxQgkAAAAAMA1ITPLqtfm79WLc3bnKh5JUkAFT/Wf+LtN8UiSthy7oP4Tf1eAj6ckyTCkOdtOuiRnoKyggAQAAAAAKPPikzM0ZvJmTVkflWfMrpPxSkjLlCTVDaqgdwa3VN2g7FFGCWmZ2n0q3oz9aetJGUbuIhRQXlFAAgAAAACUaX+dSdTAz9bpj7/OORU/qE1tLXq0u4Z2qKdFj3bXHW1q54qJikvONVIJKM8oIAEAAAAAyqw1f57VHZ+t09FzSfnG+nt7aOLdYfpwaJj8/3+6mr+PpyYMDdPEu8Pk7227TPBPW5nGBlzGItoAAAAAgDLHMAxNXhelf/+6T3aWO5IkebpZ5OPlroTUTLUPqawJQ8NUN8jXbuyAsNpqW6+ynpi1Q1uOXVAVPy/d3rpWMT4CoGyhgAQAAAAAKFPSM616Zd4ezdx8Is+Y6gHe+mpUe1UP8NGSPTEa0amePNwdT8KpG+SrmePCNT3yuG4KraHqAT5FnTpQZlFAAgAAAACUGXGJafrntG3aFHU+z5jWdQP15T3tzALQ6C71ne7fw93tquKB8oICEgAAAACgTDgQc0n3f7dFJy+k5BkzMKyW/ju4lXw83V2YGXDto4AEAAAAACj1ftsXq8dnbldSelaeMc/c2ETjIxrJYrG4MDOgfKCABAAAAAAotQzD0OdrDuu9pQdl5LFYtq+Xuz4aGqZ+LWq4NjmgHKGABAAAAAAolVIzsvT8z7s0d8fpPGNqB1bQ16Pbq1nNABdmBpQ/FJAAAAAAAKXOmUupGvf9Vu04cTHPmA71K2vSyHYK9vN2XWJAOUUBCQAAAABQquw+Ga8Hpm5RzKXUPGOGtq+rNweGysvDzYWZAeUXBSQAAAAAQKnx665oPfXjDqVmWO22u1mkl25prrFd67NYNuBCFJAAAAAAACXOajU0ccUhTVxxKM8Yfx8PfTK8rXpeX9WFmQGQKCABAAAAAEpYcnqmnpq9U4v3xOQZUz/YV1+P7qDG1fxcmBmAyyggAQAAAABKzOmLKbr/uy3aF30pz5iujYP16fC2CvT1cmFmAHKigAQAAAAAKBFbj13Qg99v1bnEtDxjRncO0Uu3NpenO4tlAyWJAhIAAAAAwOV+2XZSz/+8W+lZ9hfL9nCz6PUBLTSiU4iLMwNgDwUkAAAAAIDLZFkNvbv0gL5YcyTPmEBfT30+op06Nwp2YWYAHKGABAAAAABwiYTUDD0+c4dWHDiTZ8x11fz09ej2Cgmu6MLMAOSHAhIAAAAAoNgdj0vW/VM368/YxDxjejWpqv8NayN/H08XZgbAGRSQAAAAAADFauOROP1z2lZdSM7IM2Zcj4Z67qamcnezuDAzAM6igAQAAAAAKDYzIo/rlXl7lGk17LZ7ubvp7UEtNaRdHRdnBuBqUEACAAAAABS5zCyr/v3rfk1ZH5VnTBU/L31xTzu1CwlyXWIACoQCEgAAAACgSMUnZ+jhH7bp90Pn8oxpVjNAX49ur9qBFVyYGYCCooAEAAAAACgyh88m6oHvtujIuaQ8Y25qUUMfDm0tXy8+kgJlBe9WAAAAAECRWPvnWT00Y5sSUjPzjHm0d2M93vd6ubFYNlCmUEACAAAAABSKYRiasj5Kby7cpzzWypa3h5vev7O1bmtdy7XJASgSFJAAAAAAAAWWnmnVq/P36IdNJ/KMqR7gra9GtVerOoGuSwxAkaKABAAAAAAokPNJ6frHtK3adPR8njGt61TSl6Paq3qAjwszA1DUKCABAAAAAK7awZgE3ffdZp28kJJnzICwWnpncCv5eLq7MDMAxYECEgAAAADgqizfF6vHZm5XUnpWnjHP3NhE4yMayWJhsWzgWkABCQAAAADgFMMwNGnNEb279ICMPBbL9vVy10dDw9SvRQ3XJgegWFFAAgAAAADkKzUjSy/8sltztp/KM6Z2YAV9Pbq9mtUMcGFmAFyBAhIAAAAAwKEzCakaN3Wrdpy4mGdMh/qV9fnIdqri5+26xAC4DAUkAAAAAECe9pyK1wNTtyg6PjXPmLva19G/B7aUl4ebCzMD4EoUkAAAAAAAdv26K1pP/bhDqRlWu+1uFulftzTXvV3rs1g2cI2jgAQAAAAAsGG1GvrfykP6aPmhPGP8vT308fA2imhSzYWZASgpFJAAAAAAAKaU9Cw9/eNO/bo7Os+Y+sG++np0BzWu5ufCzACUJCaoFoH09HR9//336t+/v0JCQuTj46OaNWuqS5cuev/993Xu3Lliua5hGPrtt9903333KTQ0VIGBgfLw8FBgYKCaN2+ue+65R/Pnz1dWVlaxXB8AAADAteX0xRQNmbTeYfGoa+NgzX2oK8UjoJxhBFIhHThwQMOHD9f27dttjsfExCgmJkYbNmzQe++9p8mTJ6t///5Fdt3jx4/rnnvu0dq1a3O1xcfHKz4+Xvv379e0adPUtm1bTZs2Tc2aNSuy6wMAAAAoO6xWQwM/W6fk9Cz5erlr7viucnOzXbNo2/ELGjd1q84lpuXZz6jOIXr51ubydGcsAlDeUEAqhJMnT6pPnz46ffq0JMlisahHjx5q3Lixzpw5o+XLlyslJUVnzpzRwIEDtXjxYvXp06fQ1z1z5owiIiJ09OhR81idOnXUqlUr1axZU6dPn9bOnTvNvLZt26aePXsqMjJSDRo0KPT1AQAAAJQtB2IStOtkvLl/MDZBzWoGmPu/bDup53/ZrfRM+4tle7hZ9NrtLTQyPKTYcwVQOlFAKoQRI0aYRZqQkBDNnz9frVq1MtvPnTunu+++WytWrFBGRobuuusuHT58WIGBgYW67vPPP28Wj3x8fDRx4kTde++98vD4+39nRkaGvvzySz355JNKT0/X2bNn9fjjj2vevHmFujYAAACAsmfjkbhc+81qBshqNfTu0oOatOZwnucG+nrqsxFt1aVRleJOE0ApxrjDAlq0aJE5fczLy0sLFiywKR5JUpUqVTRv3jw1bNhQknT+/Hm9++67hbpuSkqKZs2aZe7/97//1bhx42yKR5Lk6emphx56SG+//bZ57Ndff9WFCxcKdX0AAAAAZY+9AlJiWqbGfb/FYfGocTU/zXuoK8UjABSQCurTTz81t0ePHq2WLVvajatYsaLeeOMNc/+LL75QZmZmga976NAhJScnm/vDhg1zGD9ixAhzOysrS0eOHCnwtQEAAACUPVarocij522ObTgcp0GfrtPy/WfyPK9Xk6qaM76LQoIrFneKAMoACkgFkJiYqBUrVpj7Y8eOdRg/ZMgQ+fv7S8oehWRv4euruXZO+U2Hq1y5ss2+1Wp/TjMAAACAa9OBmATFp2TYHLuUmqk/zyTmcYY0rkdDfT26g/x9PIs7PQBlBAWkAli/fr3S0rLvTFCxYkV16NDBYby3t7fCw8PN/ZUrVxb42vXq1bPZ37t3r8P4PXv2mNuenp7ciQ0AAAAoZ3JOX2tQxfFoIi93N703pJVe7N9M7lfcpQ1A+cYi2gWwf/9+c7tly5a51h+yp23btvrtt99ynX+16tSpozZt2mj79u2SpJdeeknz58+Xu7t7rtjMzEy98MIL5v6oUaPk5+dX4GsDAAAAKH3eXLhPv+6KzrM95+ijR3o31pOzd9qNc7NIfj4e+mDZn/pg2Z+6tVVNvXRr8yLPF0DZRAGpAA4ePGhuh4Q4dxvLnCOHDhw4UKjrf/jhh+rXr58yMjK0aNEitW/fXi+99JLatWunGjVqKDo6Wps3b9a///1v7d69W5LUtWtXffDBB4W6rpQ9oqlz584FOnfDhg2Fvj4AAACAvxmGoSV7YhRzKTXf2OCKXrq9dS299et+xSWl52q3GtL5HMeX7ouhgAQUk4J+rs45y8jVKCAVQFzc30NAq1ev7tQ5NWrUMLfPnz/vIDJ/ERERWrJkiYYMGaILFy5ox44dGjJkSJ7Xve+++/TKK6/Iy8urUNeVstdg2rhxY6H7AQAAAFB4F5IzdDYhLddxdzeLqvp5m/vVArz14V2t5eHuplkPhuvJ2Tt15tLf551NTFOW1bDpIzY+TeeT0hVUsfCfIwDYKoufqykgFUDOhawrVKjg1Dk5465cCLsgevfuraioKH344Yd666237N7Zzd3dXbfddpuGDRtWJMUjAAAAAKVLUEUvLX+yp/6zeL8W74kxj2dZDbWpF6gXbm6mesG+Nuc0ruav+Q93kyQdj0vW24v2a8neGJuY/i1r6IWbm1E8AmCigFQAqal/Dw91tjDj7f139T8lJaXQORw5ckRPPfWU5s2bJ8MwVL9+fXXq1EmVKlVSXFyc1q1bp5iYGH311Vf65ptv9Oqrr+qVV14p9HUBAAAAlC71gn31+ch22ngkTm8s2Kd90ZckSYv3xGjF/jOa/kAndagflOu8zVHnNeKrSKVn/X2n5uY1A/TKbc0V3jDYZfkDKBsoIBWAj4+PuZ2ennvusD2X79omOT9qKS8bN27UjTfeqEuXLikwMFBfffWVBg8eLIvl77skZGZm6osvvtBTTz2ltLQ0vfrqq/Lx8dGzzz5bqGsDAAAAKJ3CGwZrwSPd9MOm43p9wV5lZBlKz7Jq5qYTdgtIP2w6bhaPqvh565kbr9eQdnW5+xoAuyggFUDOO5k5O5ooZ1xh7oR24cIFDRo0SJcuXZLFYtHcuXPVs2fPXHEeHh566KGH5Ovrq3vvvVeS9PLLL2v48OGqU6dOga/v5+en0NDQAp8PAAAAoPhYDUNr/jyrjKy/1zPydLdfEPJ0czO3n7upie5sX7fY8wOQLTw8vEDn7dmzp0iWxSkICkgFEBz893DO2NhYp86Jifl7TnFQUO7qv7O+/PJLRUdn36KzX79+dotHOY0ZM0bvvPOODh48qPT0dE2fPl3PPfdcga8fGhrK3dQAAACAUigzy6rHZm7Xb/tsP6MMaVdHmVlWff3HUf0Zm6Drq/vr/m4NNKR9Hc3ackKSNG/HaQpIgAsV9HN1586dS2wBbgpIBdCkSRNz+9ixY06dc/z4cXO7adOmBb72kiVLzO1evXrlG2+xWBQREaGDBw9KkrZs2VLgawMAAAAonbKshp6cvVOLdtsuhl0/2FfVA3w09MuN2nrsgnl8+b5YTRgappBgXx2LS9a6w+d06mKKagcWbrkNANcut/xDcKVmzZqZ27t377Z7B7Qrbdu2ze75V+vUqVPmds6RUI7kjIuPjy/wtQEAAACUPlaroWd/2qX5O0/naguo4Kn+E3+3KR5J0pZjF9R/4u8K8PGUJBmGNGfbSZfkC6BsooBUAF26dDHvqpaUlJTvqJ60tDSbIWa9e/cu8LVzLsB9/vx5p86Ji4sztwMDAwt8bQAAAACli9Vq6F9zd+vnPIo/u07GKyEt+wvvukEV9M7glqoblP2ZIiEtU7tP/f0F809bT8owDLv9AAAFpALw8/NTnz59zP0pU6Y4jP/ll1+UkJAgSapcubJ69OhR4GvXq1fP3F65cmW+8YZhaNWqVeZ+48aNC3xtAAAAAKWHYRh6bcFe/bDpRL6xg9rU1qJHu2toh3pa9Gh33dGmdq6YqLjkXCOVAOAyCkgFNH78eHN78uTJ2rt3r9245ORkvfLKK+b+gw8+KA+Pgi891bdvX3N72bJlWrt2rcP4yZMn688//zT3b7zxxgJfGwAAAEDpYBiG3ly4X1M3OF6T1d/bQxPvDtOHQ8Pk///T1fx9PDVhaJgm3h0mf2/bzyY/bWUaGwD7KCAV0C233KLu3btLktLT03Xrrbdq9+7dNjFxcXEaOHCg/vrrL0nZd1/L6w5oUVFRslgs5s/q1avtxo0ZM8a8i5thGBo4cKB++umnXHGZmZn69NNPbQpdHTt2zPeubQAAAABKN8Mw9M6Sg/p23VG77W6W7P+2D6msRY9114Cw3KONJGlAWG0teqy72odUliRV8fPS7a1rFUvOAMo+7sJWCDNmzFDHjh0VHR2tqKgohYWFqWfPnmrYsKHOnj2r5cuXKzk5WZLk4eGh2bNnF3oNokqVKunbb7/V4MGDlZWVpQsXLujOO+9U/fr1FR4erkqVKuncuXNat26dYmL+vgNDUFCQpk6dWqhrAwAAACh5E5Yf0qQ1h+22ebm76Z0hLXUpJVMjOtWTh7vjMQN1g3w1c1y4pkce102hNVQ9wKc4UgZwDaCAVAh16tTRypUrNWzYMO3YsUNWq1WrVq2yWXNIkqpWrarJkyfbrJtUGAMGDNCCBQt03333KTo6WlL2CKaoqCi78WFhYZo+fbqaNGlSJNcHAAAAUDI+WXlI/1txyG6bh5tFn41oq77Nq19Vnx7ubhrdpX4RZAfgWkYBqZCaNm2qyMhIzZw5Uz/88IP27t2r2NhYBQYGqmHDhrrjjjt07733qkqVKkV63ZtvvllHjhzRrFmz9Ouvv2r79u2KjY1VcnKy/P39VatWLXXs2FGDBw9W//795ebGbEUAAACgLPtizWG9v+xPu23ubhZ9MrzNVRePAMBZFJCKgJeXl0aNGqVRo0YVuI/69etf9S0zfXx8NHr0aI0ePbrA1wUAAABQ+n37x1H9Z/EBu21uFmnC0DDdFFrTxVkBKE8YlgIAAAAApdj3G4/pjYX77LZZLNL7d7Zm8WsAxY4CEgAAAACUUrM2H9fLc/fk2f7fQS01qG0dF2YEoLyigAQAAAAApdAv207q+V9259n+74GhGtqhngszAlCeUUACAAAAgFJm/s7TevrHncprmdRXb2uukeEhrk0KQLlGAQkAAAAASpHFu6P1xKwdsuZRPHqxf1ON7drAtUkBKPcoIAEAAABAKbF8X6we+WG7svKoHj3d73qN69HIxVkBAAUkAAAAACgVVh88o/HTtykzj+LRo32u08O9r3NxVgCQjQISAAAAAJSwPw6d07jvtyo9y2q3/R89G+mJvhSPAJQcCkgAAAAAUII2HonT/VM3Kz3TfvHovm4N9NxNTWSxWFycGQD8jQISAAAAAJSQLVHnde+UzUrNsF88GtU5RC/d0oziEYASRwEJAAAAAErAjhMXNWbyZiWnZ9ltH9axrl67rQXFIwClAgUkAAAAAHCxPafidc83kUpMy7TbPrhtHb01sKXc3CgeASgdKCABAAAAgAvtj76kkd9EKiHVfvFoQFgtvTukFcUjAKUKBSQAAAAAcJFDsQka8XWkLiZn2G3v37KGPriztdwpHgEoZSggAQAAAIALHD6bqGFfRep8Urrd9huaV9fEu9vIw52PaQBKH34zAQAAAEAxOxaXpOFfbdS5xDS77b2aVNUnw9vIk+IRgFKK304AAAAAUIxOnE/W8K8iFXvJfvGo+3VV9PnIdvL2cHdxZgDgPApIAAAAAFBMTl9M0fCvN+rUxRS77eENg/TlPe3l40nxCEDpRgEJAAAAAIpB7KVUDf9qo06ct188ah9SWd+M7qAKXhSPAJR+FJAAAAAAoIidTUjT8K82Kiou2W57WN1ATR7bQRW9PVycGQAUDAUkAAAAAChC55PSNfLrSB0+m2S3vWXtSvru3o7y9/F0cWYAUHAUkAAAAACgiFxMzi4eHYxNsNverGaAvr+voypVoHgEoGyhgAQAAAAAReBSaoZGfbtJ+6Iv2W2/vrqfpt3XUYG+Xi7ODAAKjwISAAAAABRSYlqmRn+7SbtOxtttb1i1oqbfH65gP28XZwYARYMCEgAAAAAUQnJ6psZO3qTtxy/aba8f7KsfHghXVX+KRwDKLgpIAAAAAFBAKelZum/KFm2OumC3vU7lCprxQLiqB/i4ODMAKFoUkAAAAACgAFIzsjTu+y3acCTObnutSj764YFw1Qqs4OLMAKDoUUACAAAAgKuUnmnV+Onb9Puhc3bbqwd4a8YD4aob5OvizACgeFBAAgAAAICrkJFl1cMztmnlgTN226v4eWv6/eGqX6WiizMDgOJDAQkAAAAAnJSZZdXjM3do2b5Yu+1BFb0044FOalzNz8WZAUDxooAEAAAAAE7Ishp6+sed+nV3tN32ShU8Ne2+Trq+ur+LMwOA4kcBCQAAAADyYbUaeu7nXZq747Tddn8fD027r5Oa1wpwcWYA4BoUkAAAAADAAcMw9K+5e/TT1pN22/28PTT13o5qWaeSizMDANehgAQAAAAAeTAMQ6/N36sfNh232+7r5a7JYzuoTb3KLs4MAFyLAhIAAAAA2GEYht76db++23DMbruPp5u+Gd1BHeoHuTgzAHA9CkgAAAAAcAXDMPTu0oP6+o+jdtu9PNz01aj26two2MWZAUDJoIAEAAAAAFf4aPkhfb76sN02T3eLvhjZTt2vq+rirACg5FBAAgAAAIAcPl31lyauOGS3zcPNok+Ht1WvptVcnBUAlCwKSAAAAADw/75ae0TvLT1ot83dzaL/DWujfi1quDgrACh5FJAAAAAAQNKUdUf11qL9dtvcLNKHd7VW/5Y1XZwVAJQOFJAAAAAAlHvTNh7Tawv22W2zWKR3h7TWgLDaLs4KAEoPCkgAAAAAyrXZm0/opbl78mz/zx0tNaRdHRdmBAClDwUkAAAAAOXWnO0n9dwvu/Jsf3NAC93dsZ4LMwKA0okCEgAAAIByacHO03pq9k4Zhv32l29trns613dpTgBQWlFAAgAAAFDuLNkTo8dn7ZA1j+LR8zc31X3dGrg2KQAoxSggAQAAAChXVuyP1SM/bFNWHtWjJ2+4Xv/o2cjFWQFA6UYBCQAAAEC5sebPs/rntG3KyLJfPHqkd2M92uc6F2cFAKUfBSQAAAAA5cK6v85p3NQtSs+y2m1/sEdDPXnD9S7OCgDKBgpIAAAAAK55kUfidP93W5SWab94NLZrfT1/c1NZLBYXZwYAZQMFJAAAAADXtK3HzmvslM1Kyciy2z4yvJ5eubU5xSMAcIACEgAAAIBr1s4TFzXm281KTrdfPLq7Q129cXsoxSMAyAcFJAAAAADXpD2n4nXPN5FKSMu02z6obW29fUdLublRPAKA/FBAAgAAAHDNORBzSSO/idSlVPvFo9ta19J7Q1pTPAIAJ1FAAgAAAHBNORSboBFfRepicobd9ptDa+jDu1rLneIRADiNAhIAAACAa8aRs4ka/nWk4pLS7bb3bVZNE+9uI093PgoBwNXgtyYAAACAa8KxuCQN/ypSZxPS7Lb3vL6qPh3RVl4efAwCgKvFb04AAAAAZd7JC8ka/lWkYi6l2m3v1riKvrinnbw93F2cGQBcGyggAQAAACjTouNTNOyrjTp1McVue6cGQfpqVHv5eFI8AoCCooAEAAAAoMw6cylVw7+K1Inz9otH7UMq69sxHVTBi+IRABQGBSQAAAAAZdK5xDQN/zpSR88l2W1vXTdQk8d2UEVvDxdnBgDXHgpIAAAAAMqc80npGvl1pP46k2i3PbR2gKaO7Sh/H08XZwYA1yYKSAAAAADKlPjkDI38OlIHYhLstjet4a/v7+2kSr4UjwCgqDCWEwAAAECpYrUaGvjZOiWnZ8nXy11zx3eVm5tFknQpNUOjvo3UvuhLds+9rpqfpt3fSZUrerkyZQC45lFAAgAAAFCqHIhJ0K6T8eb+wdgENasZoMS0TI35dpN25mjLqWGVipr+QCdV8fN2VaoAUG4whQ0AAABAqbLxSFyu/eT0TN07ebO2Hb9o95yQYF/NeCBc1fx9XJAhAJQ/jEACAAAAUKpcWUBa/1ecftsXq01R5+3G1w6soBkPhKtGJYpHAFBcKCABAAAAKDWsVkORR20LRasOnlGm1bAbX7OSj354IFy1Ayu4Ij0AKLeYwgYAAACg1DgQk6D4lAybY3kVj6r5e2vGA+GqF+zritQAoFyjgAQAAACg1Mg5fa1BlYp5xlXx89KMB8IdxgAAig5T2AAAAAC4zJsL9+nXXdF5tuccffRI78Z6cvbOXDEWS/Z/R34daR67tVVNvXRr86JLFABggxFIAAAAAFzCMAwt2ROjmEupef6kZGRJkoIreun21rUUXNHLTj/SucR0m/OW7otx9cMBgHKFEUgAAAAAXOJCcobOJqTlOu7uZlFVP29zv1qAtz68q7U83N0068FwPTl7p85c+vu8s4lpyrpiXaTY+DSdT0pXkJ2CEwCg8CggAQAAAHCJoIpeWv5kT/1n8X4t3vP3iKEsq6E29QL1ws3Nci2I3biav+Y/3E2SdDwuWW8v2q8le21HG/VvWUMv3NyM4hEAFCMKSAAAAABcpl6wrz4f2U4bj8TpjQX7tC/6kiRp8Z4Yrdh/RtMf6KQO9YNynbc56rxGfBWp9Cyreax5zQC9cltzhTcMdln+AFBesQYSAAAAAJcLbxisBY900zuDW6qit7skKT3LqpmbTtiN/2HTcbN4VMXPW+8MbqkFj3SjeAQALsIIJAAAAAAlwt3NIl8vDyWlZZnHPN0tdmM93f7+7vu5m5rozvZ1iz0/AMDfGIEEAAAAoESsPnhGT8zaYXNsSLs6ysyyatKaw3py9g5NWnNYmVlWDWlfx4yZt+O0izMFADACCQAAAIDLbY46r39M26rMHHdTqx/sq+oBPhr65UZtPXbBPL58X6wmDA1TSLCvjsUla93hczp1MUW1AyuUROoAUC4xAgkAAACAS+09Ha97p2xWaobV5nhABU/1n/i7TfFIkrYcu6D+E39XgI+nJMkwpDnbTrosXwAABSQAAAAALnT0XJJGf7tJCamZudp2nYxXQlr28bpBFfTO4JaqG5Q9yighLVO7T8WbsT9tPSnDMHL1AQAoHhSQAAAAALhEdHyKRn4dqXOJ6Q7jBrWprUWPdtfQDvW06NHuuqNN7VwxUXHJuUYqAQCKDwUkAAAAAMXufFK6Rn4dqVMXU/KM8fP20MS7w/Th0DD5//90NX8fT00YGqaJd4fJ39t2CdeftjKNDQBchQISAAAAgGKVkJqhMZM36fDZpFxtbpbs/7atF6jFj3XXgLDco40kaUBYbS16rLvah1SWJFXx89LtrWsVW84AAFvchQ0AAABAsUnNyNIDU7do18l4u+3NawZoQJtaGtulgTzcHX+/XTfIVzPHhWt65HHdFFpD1QN8iiNlAIAdFJAAAAAAFIuMLKsenrFNG4+ct9vetIa/pt8frkq+nk736eHuptFd6hdRhgAAZzGFDQAAAECRs1oNPffTLi3ff8Zue0iwr6be2/GqikcAgJJDAQkAAABAkTIMQ28s3Kdftp+y217N31vT7uukakxBA4AygwISAAAAgCL10fJDmrI+ym5boK+npt3fSXWDfF2bFACgUCggAQAAACgy3/5xVBNXHLLb5uvlriljO+r66v4uzgoAUFgUkAAAAAAUiZ+2ntQbC/fZbfNyd9NXo9orrG6ga5MCABQJCkgAAAAACm3p3hg99/Muu21uFul/w9qoa+MqLs4KAFBUKCABAAAAKJT1f53TIzO2K8tq2G1/Z3Ar3RRaw8VZAQCKEgUkAAAAAAW248RFPTB1i9KzrHbbX761ue5sX9fFWQEAihoFJAAAAAAFcig2QWMmb1JSepbd9kd7N9Z93Rq4OCsAQHGggAQAAADgqp04n6yR30TqYnKG3fbRnUP0xA3XuzgrAEBxoYAEAAAA4KqcSUjVyG8iFXspzW77wLBaevW2FrJYLC7ODABQXCggAQAAAHBafHKGRn2zScfiku22921WTe/d2VpubhSPAOBaQgEJAAAAgFOS0zN173ebdSAmwW57pwZB+mR4W3m68zEDAK41/GYHAAAAkK/0TKv+MW2bth67YLc9tHaAvh7dXj6e7i7ODADgChSQAAAAADiUZTX0xKwdWvvnWbvtjapW1HdjO8rfx9PFmQEAXIUCEgAAAIA8GYahl+bu1q+7o+221w6soO/v66RgP28XZwYAcCUKSAAAAADy9N8lB/TDphN224Ireun7+zqqVmAFF2cFAHA1CkgAAAAA7Pp89WF9seaI3TZ/bw99d29HNazq5+KsAAAlgQISAAAAgFxmRB7XO0sO2G3z8XTTt2M7KLR2JRdnBQAoKRSQAAAAANhYsPO0/jV3t902DzeLPh/RTh3qB7k4KwBASaKABAAAAMC0+uAZPTFrhwwjd5vFIn04NEy9mlZzfWIAgBJFAQkAAACAJGlL1Hn9Y9pWZVrtVI8kvTkgVLe3ruXirAAApQEFJAAAAADaezpeY6dsVmqG1W77Mzc20cjwEBdnBQAoLSggAQAAAOXc0XNJGv3tJiWkZtptH9ejocZHNHJxVgCA0oQCEgAAAFCORcenaOTXkTqXmG63fWj7unrh5qayWCwuzgwAUJpQQAIAAADKqfNJ6brnm006dTHFbnv/ljX09qCWFI8AABSQAAAAgPIoITVDYyZv0l9nEu22d7+uiiYMDZO7G8UjAAAFJAAAAKDcSc3I0gNTt2jXyXi77W3rBeqLe9rJ28PdxZkBAEorCkgAAABAOZKRZdXDM7Zr45Hzdtub1vDX5DEd5evl4eLMAAClGQUkAAAAoJywWg0999MuLd8fa7c9JNhXU+/tqEq+ni7ODABQ2lFAAgAAAMoBwzD0xsJ9+mX7Kbvt1fy9Ne2+TqoW4OPizAAAZQEFJAAAAKAc+Gj5IU1ZH2W3LdDXU9Pu76S6Qb6uTQoAUGZQQAIAAACucd/+cVQTVxyy2+br5a7JYzro+ur+Ls4KAFCWUEACAAAArmE/bz2pNxbus9vm5e6mr0a1V5t6lV2cFQCgrKGAVATS09P1/fffq3///goJCZGPj49q1qypLl266P3339e5c+eKPYe1a9fqkUceUevWrVWtWjX5+Piobt266tixo8aPH68ff/xRFy5cKPY8AAAAUHos2xujZ3/eZbfNzSL9b1gbdW1cxcVZAQDKIu7NWUgHDhzQ8OHDtX37dpvjMTExiomJ0YYNG/Tee+9p8uTJ6t+/f5Ff/8iRIxo/fryWLl2aq+3kyZM6efKkNm/erM8//1zvvfeenn766SLPAQAAAKXP+r/O6eEZ25VlNey2vzO4lW4KreHirAAAZRUFpEI4efKk+vTpo9OnT0uSLBaLevToocaNG+vMmTNavny5UlJSdObMGQ0cOFCLFy9Wnz59iuz6O3fuVJ8+fRQXF2cea968uVq0aKGgoCAlJCTo4MGD2rlzpzIzM4vsugAAACjddp64qAemblF6ltVu+8u3Nted7eu6OCsAQFlGAakQRowYYRaPQkJCNH/+fLVq1cpsP3funO6++26tWLFCGRkZuuuuu3T48GEFBgYW+tpRUVE2xaM+ffroo48+UmhoaK7Y8+fPa968eapVq1ahrwsAAIDS7VBsgkZP3qSk9Cy77Y/2bqz7ujVwcVYAgLKONZAKaNGiRVq7dq0kycvLSwsWLLApHklSlSpVNG/ePDVs2FBSdiHn3XffLZLrjxs3ziweDR06VEuXLrVbPJKkoKAgjR07VjfeeGORXBsAAACl04nzyRr5TaQuJmfYbR/dOURP3HC9i7MCAFwLKCAV0Keffmpujx49Wi1btrQbV7FiRb3xxhvm/hdffFHo6WTz5s3Tb7/9JkmqV6+evvrqK7m7uxeqTwAAAJRtZxJSdc83kYq9lGa3fWBYLb16WwtZLBYXZwYAuBZQQCqAxMRErVixwtwfO3asw/ghQ4bI399fUvYopMsjlwrq888/N7efeOIJs28AAACUT/HJGRr1zSZFxSXbbe/TtJreu7O13NwoHgEACoYCUgGsX79eaWnZ3+xUrFhRHTp0cBjv7e2t8PBwc3/lypUFvvaZM2fM0UeSNHz48AL3BQAAgLIvOT1T9363WQdiEuy2d2oQpE9HtJWnO//0BwAUHH9FCmD//v3mdsuWLeXhkf9a5G3btrV7/tXasGGDrNbsu2k0adJE1apVU3x8vD744AN16tRJwcHB8vX1VUhIiO68807NnDnTjAcAAMC1JT3Tqn9M26atxy7YbQ+tHaCvR7eXjyfLHQAACoe7sBXAwYMHze2QkBCnzqlXr565feDAgQJfe/PmzeZ2ixYttGHDBt199906fvy4Tdzx48d1/Phx/fTTT3rnnXf0yy+/qEGDwt9tY8+ePercuXOBzt2wYUOhrw8AAIBsWVZDT8zaobV/nrXb3rBqRX03tqP8fTxdnBkAID8F/Vy9Z8+eIs7EeRSQCuDy3c8kqXr16k6dU6NGDXP7/PnzBb72iRMnzO34+Hj1799fFy9elJQ9yqlVq1bKysrS5s2bzULVjh071LlzZ23ZskV16tQp8LWl7PWfNm7cWKg+AAAAUDiGYeilubv16+5ou+21Ayto2n2dFOzn7eLMAADOKIufqykgFUBiYqK5XaFCBafOyRmX8/yrdblYJMlcyLtKlSqaPXu2evXqZRM7e/ZsjRkzRikpKYqNjdXIkSO1evXqAl8bAAAApcN/lxzQD5tO2G0Lruil7+/rqFqBzv07FQAAZ7AGUgGkpqaa215eXk6d4+3997c/KSkpBb52UlKSzb67u7sWLFiQq3gkSXfddZemTJli7q9Zs4YCEgAAQBn3+erD+mLNEbtt/t4e+u7ejmpY1c/FWQEArnUUkArAx8fH3E5PT3fqnMt3bZOcH7WU37UladCgQTZ3eLvSXXfdpfbt25v7M2fOLPC1AQAAULJmRB7XO0vsr6fp7eGmb8Z0UGjtSi7OCgBQHjCFrQD8/P7+RsfZ0UQ543KeX5hrS9Idd9yR7zl33HGHtmzZIklav359ga99+fqhoaGF6gMAAABXb8HO0/rX3N122zzcLJo0sp06NghycVYAgIJwNBDEkT179hRqWZzCoIBUAMHBweZ2bGysU+fExMSY20FBBf/DnvPaktS8efN8z8kZc+rUqQJfW5JCQ0O5mxoAAICLrT54Rk/M2iHDyN1msUgfDg1Tr6bVXJ8YAKBACvq5unPnziW2ADdT2AqgSZMm5vaxY8ecOuf48ePmdtOmTQt87SvPdWY0U86YhISEAl8bAAAArrcl6rz+MW2rMq12qkeS3hwQqttb13JxVgCA8oYCUgE0a9bM3N69e7cyMzPzPWfbtm12z79aV04fc6YglDOmUiXmxAMAAJQVe0/Ha+yUzUrNsNptf+bGJhoZHuLirAAA5REFpALo0qWLeVe1pKQkc32hvKSlpdkMMevdu3eBrx0eHq6KFSua+/v27cv3nJwxdevWLfC1AQAA4DpHzyVp9LeblJBq/8vKcT0aanxEIxdnBQAoryggFYCfn5/69Olj7k+ZMsVh/C+//GKOAqpcubJ69OhR4GtXqFBBN910k7k/Z86cfM/JGdOzZ88CXxsAAACuER2fopFfR+pcov07/g5tX1cv3NxUFovFxZkBAMorCkgFNH78eHN78uTJ2rt3r9245ORkvfLKK+b+gw8+KA+Pwq1d/thjj5nbv/zyi8MFtGbPnq2tW7ea+6NHjy7UtQEAAFC8ziel655vNunURft3++3fsobeHtSS4hEAwKUoIBXQLbfcou7du0uS0tPTdeutt2r3btvbqsbFxWngwIH666+/JGXffe25556z219UVJQsFov5s3r16jyv3b17dw0YMECSZLVadfvtt9uN//HHHzVmzBhzf+jQoQoLC3P+QQIAAMClElIzNGbyJv11xv4tmrtfV0UThobJ3Y3iEQDAtQo3FKacmzFjhjp27Kjo6GhFRUUpLCxMPXv2VMOGDXX27FktX75cycnJkiQPDw/Nnj1bgYGBRXLtb7/9Vl27dtWBAwd09uxZ9erVS+3atVOrVq2UlZWlzZs3a//+/WZ88+bN9eWXXxbJtQEAAFD0UjOy9MDULdp1Mt5ue9t6gfrinnby9nB3cWYAAFBAKpQ6depo5cqVGjZsmHbs2CGr1apVq1Zp1apVNnFVq1bV5MmTbdZNKqygoCCtWLFCY8aM0W+//SZJ2rp1q810tcv69++vadOmKSAgoMiuDwAAgKKTkWXVwzO2a+OR83bbm9bw1+QxHeXrxT/fAQAlg79AhdS0aVNFRkZq5syZ+uGHH7R3717FxsYqMDBQDRs21B133KF7771XVapUKfJr16pVS8uWLdPChQs1ffp0bd68WdHR0bJYLKpZs6a6d++ukSNHFuqubwAAACheVquh537apeX7Y+22hwT7auq9HVXJ19PFmQEA8DcKSEXAy8tLo0aN0qhRowrcR/369WUYRoHOvfXWW3XrrbcW+NoAAAAoGYZh6I2F+/TL9lN226v5e2vafZ1ULcDHxZkBAGCLRbQBAACAEvLR8kOasj7Kblugr6em3d9JdYN8XZsUAAB2UEAC8H/s3Xd0VHX6x/H3pDcgJHQIkNB76AldQEVARUEB6SWg2N3V/VlWV13dddd1FxQVAgKCNFFAQFE6oYQSegkCCR0ChJZC2sz8/rgaRCfJEDKT9nmdM4dJvs/3zpM9Z83kM/c+V0RERArBF5vimbjmqM01Hw9XZoxsS/3KZZzclYiIiG0KkEREREREnOybmDO8s/yQzTUPVxcih7ehZc3yTu5KREQkZwqQRERERESc6KeDF3jlm30211xMMGlwSzrWLfgbsIiIiNwNBUgiIiIiIk6y5dhlnpm7G7PF9s1TPujfnF5Nqzi5KxERkbwpQBIRERERcYK9p68R8eVOMswWm+t/7duYx9oEObkrERER+yhAEhERERFxsKMJSYyYsZ2UDLPN9ee612VMp2AndyUiImI/BUgiIiIiIg50+koqQ6dv41pqps31EeG1ePHe+k7uSkRE5M4oQBIRERERcZCLSWkMm76NhBvpNtf7hVbjrQebYDKZnNyZiIjInVGAJCIiIiLiANdTMxk+fTsnElNtrvdoWIl/P9YCFxeFRyIiUvQpQBIRERERKWCpGVmMnrWD2AtJNtfbBwcweUgr3F31dlxERIoH/cYSERERESlAGVkWnpyzi5iTV22uN61elmkj2uDl7urkzkRERPJPAZKIiIiISAExW6y8uGAPG3++ZHM9pKIvs0a1o4yXu5M7ExERuTsKkERERERECoDVauWNJftZsf+8zfXq/t7MGdOeQD9PJ3cmIiJy9xQgiYiIiIgUgA9WHmHe9tM21wJ9PZg9ph3V/L2d3JWIiEjBcCvsBkQKTFYGHFkBx1bDub2QeBSy0sHNEwLrQbUWULcnNOgDbh6F3a2IiIiUIJ+tP87nG47bXCvj6cas0e0Iqejn5K5EREQKjgIkKf7MmbB1svFIufjH9aw0SNhvPHbPAb/KEDYBwp8GV80fEBERkbszd9spPlgZa3PN082F6SPb0rR6OSd3JSIiUrAUIEnxdvEwLB4P5/fe/n2TKwTWBU8/SE+GxGNgNRtryQmw+i04+C08MgUqNXJ+3yIiIlIiLNt7jteX7Le55uZi4vOhrWkXHODkrkRERAqeAiQpvk5tg68GQPoN42uTCzTsC21GQVAYePjcqs1IhdPRsHMGxC4Hq8UInabfB0MWQc32hfMziIiISLG1/shFXlq4B6v1j2smE/zn8Rbc07CS8xsTERFxAAVIUjxdPHx7eFSxIfT7FKq3tl3v4QN1uhuPszGwZAJcijX2fzUAxqyCSg2d17+IiIgUaztPXOHJOTFkmm2kR8C7Dzfl4dDqTu5KRETEcXQXNil+zJnGZWu/hkch3SBibc7h0e9Vb23Uh3Qzvk6/AYvHGccVERERycOhczcYNXMHaZkWm+sv39+AoWG1nNyViIiIYylAkuJn6+RbM48qNoRBc8HD986O4eFr7Kv4y1lH5/caxxURERHJRfzlFIZ/sY2ktCyb6+O6hDChWx0ndyUiIuJ4CpCkeMnKgOhPjecmF+OytTsNj37l4QsPf2ocB4zj6iwkERERycH56zcZOm0bl5MzbK4PbBPEqw80xGQyObkzERERx1OAJMXLkRXGXdTAGJht72VrOanRGhr2MZ4nJxgDtkVERER+50pKBsOmb+fstZs213s3q8L7jzZTeCQiIiWWAiQpXo6tvvW8zaiCOWab0b85/pqCOaaIiIiUGElpmYycsZ1jF5NtrneuV4H/DgzF1UXhkYiIlFwKkKR4OffL7COTK9QML5hjBoXduozt/J6COaaIiIiUCGmZZiK+3Mm+M9dtrres6c/nQ1vj6ebq5M5EREScSwGSFC+JR41/A+uCu3fBHNPDxzgewOVjBXNMERERKfayzBaembub6LgrNtcbVinDzJHt8PV0c3JnIiIizqcASYqXrHTjX0+/gj2uxy/Hy0or2OOKiIhIsWSxWHll0T5WH06wuV4zwIcvR7ejnI+7kzsTEREpHAqQpHhx8zT+Tbc9gyDfMn45nptXwR5XREREih2r1co7yw/x7e6zNtcrlfHkq7HtqVRW7xtERKT0UIAkxUtgPePfxGOQafsuKHcsI9U4HkCFugVzTBERESm2Jq45yswtJ2yu+fu4M2dse4ICfJzblIiISCFTgCTFS7UWxr9WM5zaWjDHPB0NVovxPFABkoiISGn2xaZ4/rf6qM01Hw9XZoxsS/3KZZzclYiISOFTgCTFS92et57vnFEwx9z5xa3nh5fBD/8HKZcL5tgiIiJSbHwTc4Z3lh+yuebh6kLk8Da0rFneyV2JiIgUDQqQpHhp0Af8KhvPY5fD2Zi7O96ZGIhdcetrSxZs+wwmhsKGfxX8rCUREREpkn46eIFXvtlnc83FBJMGt6Rj3QpO7kpERKToUIAkxYubB4RNMJ5bLbBkAmSk5O9YGSmwdMKty9duW0uCde/BpJawPRLMmfnvWURERIq0Lccv88y83ZgtVpvr/+zfnF5Nqzi5KxERkaJFAZIUP+FPQ9VfZiFdioX5T9x5iJSRYuy7FJt7XcpF+P7PMLkdHPgGLDbCJhERESm29p6+RsSsnWRk2f4d/0afRjzeJsjJXYmIiBQ9CpCk+HF1h0emgGdZ4+u49RDZ3bgczR5nYoz6uPW3jpfX/xWuxMGi0RB5Dxxfl8/GRUREpCg5mpDEyBnbSckw21x/rntdxnYOcXJXIiIiRZMCJCmeKjWCIYtuhUiXYmF6T1gwFI6vhYzU2+szUo3vLxhq1P165pFnORixAp7ZDo0eyvt1z++B2f3gy35wbk/B/TwiIiLiVKevpDJs+nauptq+TH14eC1evLe+k7sSEREputwKuwGRfKvZHsasgsXj4PxeY5bR4WXGw+QCgXXBww8ykiHx2B9nHVVtAY9MhUoNja8HzoYzO2HVW3ByU+6vHbcOpq6Dpv2h+xsQoE8nRUREiouLSWkMm76NCzfSbK73C63G3x5sgslkcnJnIiIiRZfOQJLirVJDGLsGer596+5sYIRFl3+Gc7uMf38bHvlVNurHrrkVHv2qRhsYudw4u6ly07xf/8A38ElbWPFnSL5YMD+TiIiIOMz1m5kMn76dE4mpNtd7NKzEvx9rgYuLwiMREZHf0hlIUvy5ukOnF4zh2rHL4dga41Kzy8cgKw3cvKBCXagaCnV7QMO+v8w9yoHJBPXuhTo9YP/XsO7vcO1UzvWWLNgRCXvmQodnIPwZ8CpbwD+kiIiI3K3UjCxGz9xB7IUkm+vtggOYPKQV7q76jFVEROT3FCBJyeHqDk0eMR4FwcUFWgyEJv1g5xew8d+QmphzfWYKbPgAdkyDLq9Am1Hg5lkwvYiIiMhdyciy8OScXcScvGpzvWn1skwf0QYvd1cndyYiIlI86OMVkby4eULYU/DcHiMYcvfNvT41EVb+BT5pA/sWgsX2bYFFRETEOcwWKy8u3MPGny/ZXA+p6MusUe0o45XLGcoiIiKlnAIkEXt5lYXur8Nzu6HtWHDJ4wS+a6fg2wiY0gWOrgar1Tl9ioiISDar1cobS/azYt95m+vVynkxZ0x7Av101rCIiEhuFCCJ3KkylaHPf+Dp7dDk0bzrE/bDV/1h1oNwJsbx/YmIiEi2D1YeYd720zbXAn09mDO2PdX8vZ3clYiISPGjAEkkvwLrwGMzYNx6COmWd/2JKJjWHRYONwZ8i4iIyF2zWKw89Mkmen60gYc+2YTFcuuM38/WH+fzDcdt7ivj6cas0e0IqejnrFZFRESKNQVIInerWksYvhSGLYaqLfKuP7QUJreDZc/DDdun04uIiIh9Yi8kse/MdY5dTGbfmescSTDusDZ32yk+WBlrc4+nmwvTR7alafVyzmxVRESkWFOAJFJQ6nSHiPUw4AsoH5x7rdUMMTNhUktY8w6kXXdGhyIiIiVOdFziH75etvccry/Zb7PezcXE50Nb0y44wBntiYiIlBgKkEQKkosLNO1vzEfq/SH4Vsy9PusmRP0HJraALR9DZppz+hQRESkhfh8gLd97jpcW7rF57wqTCf7zeAvuaVjJSd2JiIiUHAqQRBzBzQPaRcBze+Ce18Ejj/kKN6/CT2/Ax61hz1ywmJ3SpoiISHFmsVjZFn/ltu/FnLpGptn2nU/febgpD4dWd0ZrIiIiJY4CJBFH8vSDrq/A83uh/VPg4p57/Y0zsOQp+LwTHFmJzY9PRUREBDDmH12/mWlX7cv3N2BYWC0HdyQiIlJyFUqANGnSJCZNmsTHH39MZqZ9v/RFijXfCvDAP+HZndB8IGDKvf7iIZg3EGY8AKe2OaVFERGR4ua3l68FV/DNsW5clxAmdKvjjJZERERKLLfCeNEXXngBk8lEq1atePbZZwujBZHCUb42PDoVwp+BNW/DsdW515/aCl/cBw36QI83oVJDp7QpIiJSFLy7/BAr9uV8x9Lfnn30bPe6vLRw7x9qvN1d+W7POb7bcy77e32bV+WNvo0LtlkREZESrlDOQPL1NT4hatq0aWG8vEjhq9ochn4DI5ZB9dZ51x9ZAZ+Fw9Kn4foZx/cnIiJSyKxWKysPXODCjbQcHzczjZmBgb4ePNSiGoG+Hn84zs1M8x/2/XjogrN/HBERkWKvUAKkqlWrAmAy5XEZj0hJF9wFxq6Bx7+EwLq511otsHuOMWj7p79C6pXc60VERIqxq6mZXEpK/8P3XV1MVCnrlf1oXqMcC8aH4ebqwoLxYTSvUe62dVeXP77fTLiezpWUDGf8GCIiIiVGoVzC1qZNG44dO8bhw4cL4+VFihaTCRo/bFymtns2rP8nJOfyyWhWGmyZBLtmQacXof2T4O7tvH5FREScIMDXg9UvdeUfPxzmhwO3fi+aLVZa1vTn1QcaUTPQ57Y9dSuV4btnOgFwKjGV978/zMqDt/9O7d2sCq8+0IgAG2criYiISM4K5QykYcOGAbBjxw4OHTpUGC2IFD2ubtBmFDy325h35Fku9/q067D6bzCpFcTMAnOWU9oUERFxlpqBPnw2tDXzx4XRuGrZ7O//cOACPT/awI4Tts/G3XHiCj0/2nBbeNS4alnmjwvj0yGtCQrwsblPREREclYoAdIDDzxAv379sFgsDBkyhKtXrxZGGyJFk4cPdP4TPL/HGLbtmscnpEnnYNlzxoykw8vAanVKmyIiIs4SFhLIsmc78ZdeDfj1irQMs4X520/brJ+3/RQZZgsAFfw8+aB/M5Y924mwkEBntSwiIlLiFEqABDBz5kz69u3L3r17adq0KVOnTuXatWuF1Y5I0eMTAPe/B8/ugtAhQB4zwy7/DAuGwvR74cRmp7QoIiLiLKeupPLl1pNYfvM5ibur7d+N7i633uL+pVcDBrataXMWkoiIiNivUGYgde/eHTDuruHm5sb58+d56qmnmDBhArVr16ZixYp4e+c908VkMrFmzRpHtytSuPyDoN+nxtlIa96Bn3/Ivf7MDpjZG+rdDz3fgspNnNOniIiIgxy7mMQTkdu4+Luh2gNa1yDLbGHapnh+TkiifuUyjO0UzIA2NViw0zg7aemeczzWJqgw2hYRESlRCiVAWr9+/W13YPv1ucViIT4+nvj4+DyPYbVadRc3KV0qN4Yn5sPJrbD6LTi9Lff6oz/C0Z+gxSC45zXwr+mcPkVERArQkQtJDJkWzeXk2++aVjvQh8plvRg4NZqYk7fGIaw+lMB/B4ZSK9CHk4mpbD5+mbPXblLdXzecEBERuRuFdgmb1Wr9wyOn7+dUK1Iq1QqH0T/CoLlQsWEexVbYOw8+bg0rX4OURKe0KCIiUhAOnrvOoKlb/xAeAZT1dqf3xKjbwiOAnSev0ntiFGW93AFjNODiXWec0q+IiEhJVihnIK1bt64wXlak5DCZoGEf4zK1vfNg/T/gxtmc680ZED0Zds+Gjs9B2ATw8HVevyIiIndo35lrDJu+nes3M3NYv579PCjAm2fuqcsn645x+spNktKz2H/21vqimDM8fU9dnb0uIiJyFwolQOratWuBHOfkyZMFchyRYsvVDVoNg2YDYPtUiPoI0q7lXJ9+A9b+HbZHQte/QKvh4OrutHZFRETsEXPyKiO/2E5SelaetY+2rM7bDzehjJc7vZtV5c2lB1m8+/YPVU4kphJz8iptagc4qmUREZESz6GXsO3Zs8dhxz527FiBBVEixZ67N3R8Hp7fAx1fADev3OuTE2DFSzC5PRxcbJzfLyIiUgRsj7/C8Onb8gyPyni6MXFQKB8NDKXML5erlfFy578DQ5k4KJQynrd/TrooRpexiYiI3A2HBkj33Xcfhw4dKvDjHjlyhK5du3L69OkCP7ZIseZdHu59G57dZZxdZMrj/+JXjsPXIyHyHojb4JQWRUREcrLl+GVGfLGdlAyzzXVPN+P3Wpta5fn++c48HFrdZt3DodX5/vnOtKlVHoAKfh481KKaY5oWEREpJRwaIF2+fJmePXty9OjRAjvm/v376dq1K+fPny+wY4qUOOWqw0Mfw4RoaNg37/pzu+HLh2D2I3B+r+P7ExER+Z2NP19i1Iwd3My0HR71aFiJn17swtsPNWH+uDCCAnxyPV5QgA/zx4Xx9kNNWPFcZzrUreCItkVEREoNh9+FLSEhgR49enDixIm7PtauXbvo3r07ly5dAsDV1fWujylSolVsAIO+gjGroVbHvOuPr4UpXWDRGLgS7/j+REREgLWxCYydtZP0LIvN9fubVOazoa2pFejLiA61cXO17y2sm6sLIzrUpnLZPC7tFhERkTw5NEAKDAwE4MyZM3Tv3p0zZ/J/7fm2bdvo2bMniYmJWK1W3N3dmTdvXkG1KlKyBbWFkSvgia+hUpO86w8sgk/awvevQPIlx/cnIiKl1o8HLzB+dgwZZtvhUd/mVfnkiVZ4uDn8c08RERHJhUN/E69atQp/f39MJhMnTpygR48eJCQk3PFxoqKiuO+++7h27RoAnp6eLFq0iP79+xdwxyIlmMkE9e+DJ6PgkSlQLij3eksmbJ8Ck0Jh/T8hPckpbYqISOmxYt95nv5qF5lm2zdzeKRldf43MBR3O884EhEREcdx6G/j0NBQVq5cSZkyZTCZTBw9epTu3btz+fJlu4+xZs0aevfuTVKS8cert7c3S5cu5cEHH3RU2yIlm4srtBgEz+yE+98H7zxuaZyRDOv/ARNDYdsUyMpwSpsiIlKyLdl9lmfn7SLLYjs8eqx1DT58rIXdl6uJiIiIYzn8N3Lbtm1ZsWIFPj4+mEwmDh8+zL333svVq1fz3PvDDz/w0EMPkZKSAoCvry8rVqzgvvvuc3TbIiWfuxeEPw3P74EuL4N77sNISb0MP7wCk9vC/kVgsX2pgYiISF6+3nmaFxfuIYfsiCHta/JB/+a4upic25iIiIjkyCkf6XTs2JHvvvsOLy8vTCYTe/fu5f777+fGjRs57lm6dCmPPvooN2/eBKBMmTKsXLmSbt26OaNlkdLDqxx0fwOe2w1tRoMpj+H0V0/AN2Ngalc4thqsObz7FxERsWHutlO8vGhfjr8+Rnaozd/7NcVF4ZGIiEiR4rRzgu+55x4WL16Mh4cHJpOJmJgYevfunX120W8tXLiQxx9/nIwM41IZf39/Vq9eTceOdtxFSkTyp0wV6PtfeGYHNHkk7/oL+2BOf/jyITgb4/j+RESk2Ju15QSvLd6f4/q4LiG89WBjTCaFRyIiIkWNUy8qv++++/j6669xc3MDYOvWrTz44IOkpaVl18yePZuhQ4eSmZmJ1WolMDCQtWvX0rZtW2e2KlJ6BdaBx2ZCxDoI7pJ3ffxGiOwOC0dA4nGHtyciIsXTtKg43vruYI7rT99Th1cfaKjwSEREpIhy+lTCvn37MnfuXFxcjJfesGED/fr1IyMjg2nTpjFq1CjMZjMAlStXZv369YSGhjq7TRGp3gqGfwdDv4UqzfKuP7QEPmkLy1+EpAsOb09ERIqPyeuO8fcVh3Ncf7Fnff58XwOFRyIiIkVYodzWon///syaNSv7TcKqVato27YtTz75JBaLBavVSvXq1dmwYQNNmjQpjBZFBMBkgro9YNxG6D8d/GvlXm81w84vYFJLWPMupF13Tp8iIlIkWa1W/rf6Z/7945Eca17p1YDne9ZTeCQiIlLEFdp9UZ944gkiIyOzv96/fz+WX+7qVLNmTTZs2ED9+vULqz0R+S0XF2g2AJ7ZCQ/8G3wq5F6fmQpRH8LEUNg6GbLSndKmiIgUHVarlQ9/OsL/Vh/NseaNPo2Y0K2uE7sSERGR/Cq0AAlg1KhRTJ48GavVmv2pU506dYiKiiIkJKQwWxMRW9w8oP04eH4PdHsVPPxyr795BX58DT5uA3vmgcXslDZFRKRwWa1W3v/+MJPX5Twb7+2HmjC2s97viYiIFBdujjz4O++8Y1ddo0aNOHz4MCaTie7duzNjxgy7X+PNN9/Mb3sikl+eZaDb/0GbMbDx38Zla5bMnOuvn4IlT8KWj6HnW1DvPuPyOBERKXGsVitvLzvEzC0nbK6bTPBev2Y80b6mcxsTERGRu2KyWq1WRx3cxcXF4dez/zpwWwpeeHg40dHRt30vLCyMrVu3FlJHUmRdiYd178H+r+2rr9URer4NQbq7oohISWKxWHlj6QHmbjtlc91kgn/1b85jbYKc3JmIiEjJUJh/pzv8Ejar1eqwh4gUEQHB0H8ajN8IdXrkXX9yM0zvCfOHwKWfHd+fiIg4nNli5S/f7MsxPHIxwX8fD1V4JCIiUkw59BK2Ll266I4aIqVJ1RYw7FuI2wCr34Jzu3Ovj10OR76HlkONmUplqzmnTxERKVBZZgt//novS/acs7nu5mJi4qCW9Gle1cmdiYiISEFxaIC0fv16Rx5eRIqqkK4QsQ4OLYE178KVnIeoYrXAri9h30Jo/yR0egG8yzurUxERuUuZZgsvLNjDin3nba67u5r45IlW3N+kipM7ExERkYJUqHdhE5ESzGSCJo/A09ugz0fgVzn3+qw02Pw/mBgKmydC5k1ndCkiInchI8vCM3N35Rgeebi5MGVYa4VHIiIiJYACJBFxLFd3aDsGntsN3d8Az7K516ddg1VvwsetYddsMGc5pU0REbkzaZlmnpwTw48HE2yue7q5MG14G7o3zOMDBBERESkWFCCJiHN4+EKXl+G5PRD2NLh65F5/4yx89wx81gFiV4AG54uIFBlpmWYivtzJ2tiLNte93V2ZMbItXepXdHJnIiIi4igKkETEuXwDodf78GwMtBgM5DFo//IRmP8EfHE/nHT8rSlFRCR3qRlZjJ65g6ijl22u+3q4Mmt0OzrUreDkzkRERMSRFCCJSOHwrwmPfA5PbYZ69+ddf3obzOgFcwdBwiHH9yciIn+QnJ7FyC92sOV4os31Mp5uzB7bnnbBAU7uTERERBxNAZKIFK7KTWDIQhj5PdRom3f9zz8Yl7UtfgqunXZ8fyIiAsCNtEyGT9/G9hNXbK6X83bnq4j2tKqpO2mKiIiURAqQRKRoqN0RxqyCgV9Bhfp5FFth71xj0PaPr0Oq7T9mRESkYFxPzWTotG3sOnXN5nqArwdzI9rTvIa/U/sSERER51GAJCJFh8kEjfrCU1vhwUlQplru9eZ02PoJTGwBGz+EjBTn9CkiUopcSclgcGQ0+85ct7lewc+DeRFhNKlWzsmdiYiIiDMpQBKRosfVDVqPgOd2Qc+3wSuPP0rSb8Dad2FSK9g5A8xZzulTRKSEu5SUzuCp0Rw6f8PmeqUynswfF06DKmWc3JmIiIg4mwIkESm63L2h0wvw/F7o+Dy4euZen3wBlr8An7aHQ0vBanVGlyIiJdLFG2kMmrqVIwlJNterlvNiwfhw6lbyc3JnIiIiUhgUIIlI0eddHu59xzgjqeVQMOXxn67EY7BwOEzrAfFRzulRRKQEOX/9JgOnRnP8ku1Lg6v7e7NwfDjBFXyd3JmIiIgUFgVIIlJ8lKsBD082ZiQ16JN3/dkYmNUX5vSHC/sd35+ISAlw+koqj0/ZSvxl2+FRrUAfFj4ZTlCAj5M7ExERkcKkAElEip9KDWHwXBj9E9QMz7v+2Gr4vDN8EwFXTzi8PRGR4upkYgqDpkZz+spNm+shFXxZMC6c6v7eTu5MRERECpsCJBEpvmq2h1E/wOAFUKlxHsVW2L8QPm4DP/wFUi47pUURkeLi+KVkBk6J5uw12+FRvUp+zB8fRpVyXk7uTERERIoCBUgiUryZTNCgFzy5Cfp9BmVr5F5vyYRtn8PEUFj/AaQnO6VNEZGi7GhCEoOmRnPhRprN9YZVyjB/XBiVyig8EhERKa0UIIlIyeDiCqFPwLMxcN97xuDt3GQkwfr3YVIobI+ErAyntCkiUtQcPn+DQVOjuZSUbnO9afWyzIsII9AvjzthioiISImmAElEShZ3L+jwDDy/Fzr/CdzymNORcgm+/zNMbgf7F4HF4pw+RUSKgANnrzM4MprEFNsheosgf74aG0Z5Xw8ndyYiIiJFjQIkESmZvMpBjzfhud3QehSYXHOvvxoP34yByG5wfK1TWhQRKUx7Tl/jichorqVm2lxvU6s8c8a0o5y3u5M7ExERkaJIAZKIlGxlq8KD/4Ont0Hjh/OuP78XZj8CXz4M53Y7vD0RkcIQc/IKQ6dt40Zals319sEBzBrdjjJeCo9ERETEoABJREqHCvXg8S9h7Fqo3Tnv+rj1MLUbfD0KEo87ujsREaeJjktk2PTtJKfbDo861a3AzFHt8PV0c3JnIiIiUpQpQBKR0qVGaxixDIZ8A5Wb5V1/8FtjPtKKP0FSguP7ExFxoM3HLjNyxnZSM8w217s1qMi0EW3w9sjjsl8REREpdRQgiUjpYzJBvZ4wfiM8Ggn+NXOvt2TBjmkwqSWsfQ/SbjinTxGRArT+yEVGz9xBWqbtmwX0bFSZKcNa4+Wu8EhERET+SAGSiJReLi7Q/HF4Zif0+gB8AnOvz0yBjf+CSaEQ/Rlk2b7ltYhIUbP6UALjvowhPct2ePRA0yp8OqQVnm4Kj0RERMQ2BUgiIm6eEPYkPLcHuv4F3H1zr09NhJX/B5+0gb0LwGL7DzIRkaLgh/3neXJODBlm2/+teqhFNT4e3BIPN70tFBERkZzpnYKIyK+8ysI9r8Hze6BtBLjkMUD22ilYPA6mdIajq8BqdUqbIiL2+m7vOZ6Zt5ssi+3/Pj3aqjr/HRiKm6veEoqIiEju9G5BROT3/CpBnw/h6e3QtH/e9QkH4KsBMLMvnNnp+P5EROzw7a4zvDB/N+YcwqNBbYP4cEALXF1MTu5MREREiiMFSCIiOQmsAwO+gHEbIOSevOtPboJpPWDBULh81PH9iYjkYOGO0/zp673kkB0xLKwW7z/SDBeFRyIiImInBUgiInmpFgrDl8CwJVA1NO/6w8tgcntY9jzcOO/Y3kREfmd29Ele+WZfjlfVju4YzDsPN1F4JCIiIndEAZKIiL3q3AMR62DADCgfnHut1QwxM2FSS1j9Nty85owORaSU+2JTPH9dciDH9Se71uGvfRthMik8EhERkTujAElE5E64uEDTR+GZHdD7Q/CtlHt91k3Y9BFMbAGbJ0FmmnP6FJFSZ8qG47yz/FCO6891r8tfejVQeCQiIiL5ogBJRCQ/XN2hXQQ8txvueQM8yuRen3YNVv0VPm4Nu78Ci9kpbYpI6fDxmqP844fYHNf/dG99XrpP4ZGIiIjknwIkEZG74ekHXV+G5/dA+6fAxT33+htnYOkE+KwjxH5PjkNKRETsYLVa+einI/xn1c851rz6QEOe7VHPiV2JiIhISaQASUSkIPhWgAf+Cc/GQPNBQB6f8l86DPMHwxe94FS0U1oUkZLFarXywcojTFp7LMeaN/s2ZnzXOk7sSkREREoqBUgiIgWpfC14dAo8uQnq3Zd3/elo+OJ+mDcYLh52fH8iUiJYrVbeXX6Yzzccz7Hm3X5NGd0pj4H/IiIiInZSgCQi4ghVmsKQr2HEcqjeOu/6I9/DZx1gydNw/Yzj+xORYstisfLm0oN8sTne5rrJBB/0b8awsFpO7kxERERKMgVIIiKOFNwZxq6Bx2dDYN3ca60W2DMHJrWCn96A1CvO6VFEig2Lxcpri/czO/qkzXUXE3w4oAUD29Z0cmciIiJS0ilAKgAZGRnMnj2b3r17U6tWLby8vKhatSodOnTgww8/5PLly07t59FHH8VkMmU/unXr5tTXF5HfMZmg8UMwYRs8OBH8quReb06HLR/DxFCI+ggyUp3SpogUbWaLlZcX7WP+jtM2111dTPxvUEv6t67h5M5ERESkNFCAdJdiY2MJCwtj+PDh/PDDD5w6dYr09HQuXLjA1q1befnll2nSpAnff/+9U/r55ptvWLx4sVNeS0TukKsbtB4Jz+2GHm+BZ7nc69Ovw5q34eNWEDMTzFnO6FJEiqAss4WXFu7hm122L3F1czHxyeCWPNSimpM7ExERkdJCAdJdOHPmDD169GD37t0AmEwmunbtypgxY3jwwQfx9vYG4OLFi/Tr1481a9Y4tJ9r167xzDPPOPQ1RKQAePhA55fg+T3Q4Vlw9cy9Puk8LHsePg2DQ9+B1eqUNkWkaMg0W3hu/m6W7jlnc93D1YXPhrbmgWZVndyZiIiIlCYKkO7CkCFDOHfOeDNXq1Yt9uzZw/r165k2bRrfffcdp06dokePHgBkZmby+OOPc+3aNYf18+c//5kLFy7g7u7OgAEDHPY6IlJAfALgvr/DszEQOhRMefwnOfEoLBwG03rCiU3O6VFEClV6lpkJX+3i+/0XbK57uLkwdXhr7m1c2cmdiYiISGmjACmfvv/+ezZu3AiAh4cHy5Yto3nz5rfVVKhQgaVLlxISEgLAlStX+Ne//uWQftauXcv06dMBeOWVV2jSpIlDXkdEHMA/CPpNhqe2QIPeedef3Qkz+8BXj8GFA47vT0QKRVqmmSdnx7DqUILNdS93F74Y0ZZuDSo5uTMREREpjRQg5dPkyZOzn48YMYJmzZrZrPP19eWdd97J/nrKlClkZRXsHJObN28ybtw4AOrWrcsbb7xRoMcXESep1AgGz4NRKyEoLO/6oz/B553g2/Fw1fYdmUSkeLqZYWbsrJ2sO3LJ5rqPhyszR7WjU70KTu5MRERESisFSPmQnJx82zyjUaNG5Vo/YMAAypQpAxhnIf165lJBeeuttzh+/DgAn332GV5eXgV6fBFxslrhMHolDJoHFRvmUWyFffPhkzaw8lVISXRKiyLiOCnpWYyauZ1Nx2zfxdXP040vR7cjLCTQyZ2JiIhIaaYAKR+2bNlCeno6YJxh1LZt21zrPT09CQu7dTbB2rVrC6yXmJgYPvroIwCGDh1Kz549C+zYIlKITCZo2Nu4rO3hyVC2eu715gyI/hQmtoAN/4aMFOf0KSIFKiktkxFfbCc67orN9TJebswZ2542tQOc3JmIiIiUdgqQ8uHw4cPZz5s1a4abm1uee1q1amVz/93Iyspi7NixmM1mAgICsoMkESlBXFyh5VBj0Pa974KXf+71GUmw7u8wMRR2TANzpjO6FJECcP1mJsOmb2fnyas21/193JkXEUZokL9zGxMREREB8k4+5A+OHDmS/bxWrVp27alZs2b289jY2ALp48MPP2TPnj0A/Pvf/6ZixYoFctzcHDhwgPDw8Hzt3bp1awF3I1KKuHtDx+eg1XDY/D+I/hyybuZcn3IRVvwJtk6G7n+Fxv3ARZ8ZiBRV11IzGDp9GwfO3rC5HuDrwVdj29OoalkndyYiIiKOkN+/qw8cKLyb6ChAyofExFszRipXtu+2uVWqVMl+fuWK7dPS78TRo0d5++23AejatWuec5gKSnJyMtHR0U55LRGxwdsfev4N2o2D9f+E3XPAas65/kocLBoF1SYZ+0K6OadPEbFbYnI6Q6ZtI/ZCks31Cn6ezI1oT/3KZZzcmYiIiDhKcfy7Wh9H50NycnL2c29vb7v2/Lbut/vzw2q1EhERQVpaGh4eHnz++eeYTKa7OqaIFDNlq8FDk2BCNDR6MO/6c7vhy4dh9iNwfq/j+xMRu1xMSmPQ1Ogcw6PKZT1ZMD5M4ZGIiIgUOgVI+ZCWlpb93MPDw649np6e2c9v3szlshM7REZGsmHDBgBeffVVGjbM6y5NIlJiVawPA+fAmNVQq1Pe9cfXwpQusGi0cXaSiBSaC9eN8OjoRdsfLFX392bh+HDqVPRzcmciIiIif6QAKR+8vLyyn2dkZNi159e7toH9Zy3Zcu7cOV555RUA6tevz6uvvprvY4lICRLUFkYuhyGLoFKTvOsPfAOftIXvX4bki47vT0Ruc/baTQZO3UrcJdt3TAwK8Gb+uDBqBfo6uTMRERER2zQDKR/8/G59Emjv2US/rfvt/jv19NNPc/36dQCmTJly25lNzuDn50fTpk2d+poiYieTCerdC3W6w/6vYe17cP1UzvWWLNg+FXZ/BR2egQ7PgqcukxFxtNNXUhkcGc2Zq7bfQwRX8OWrse2p5p//D5xERESkaAsLC8vXvgMHDtz1WJz8UoCUD4GBgdnPExIS7Npz4cKF7OcBAQH5et2lS5eyZMkSAEaOHEm3bt3ydZy70bRpU91NTaSoc3GFFoOgySOw8wvY8C+4mcvw/swU2PAB7JgOXV+B1qPAzb7Lc0Xkzpy4nMITkdGcu55mc71ORV/mRoRRuayXzXUREREpGfL7d3V4eHihDeBWgJQPDRo0yH5+8uRJu/acOnXrLID8zizavXt39vPt27fnmlieOXMm+/muXbtuq/30009p1apVvnoQkWLEzRPCnoLQIbDlY9j6CWSm5lyfehl+eAW2Tobuf4Wm/cFFVzqLFJRjF5N5IjKai0npNtcbVC7DnLHtqVjGuWcXi4iIiNhDAVI+NGrUKPv5/v37ycrKws0t9/8pd+3aZXN/fh06dMju2qSkJLZt25b99Y0bN+769UWkGPEqC91fh7ZjjTONds0yLl/LybWT8O1Y2DIRev4N6vQwLo8TkXw7ciGJIdOiuZxse3Zi46plmTO2PQG+OvtPREREiiZ9tJwPHTp0yJ49lJKSws6dO3OtT09Pv+0Us+7duzu0PxERm8pUhr4fwdPbjcvb8nJhP8zpD7MehLMxju9PpIQ6dO4GgyNzDo+a1yjH3AiFRyIiIlK0KUDKBz8/P3r06JH99cyZM3Ot//bbb0lKSgKgfPnydOnSJV+v+7e//Q2r1WrX46233sre17Vr19vWCmN2kogUIYF14LGZELEOgrvmXX8iCiK7w8LhcPmYw9sTKUn2n7nO4MhorqTYDo9a1vRnztj2+PsoPBIREZGiTQFSPk2YMCH7+YwZMzh48KDNutTUVN58883sr8ePH5/n5W4iIk5RvRWM+A6GLYaqLfKuP7QUJreDZS9A0oU8y0VKu12nrvLEtGiu38y0ud62dnlmj2lPWS93J3cmIiIicucUIOVTnz596Ny5MwAZGRn07duX/fv331aTmJhIv379OHbM+MQ+ICCAv/zlLzaPd+LECUwmU/Zj/fr1Du1fRCRbne4QsR76T4fytXOvtZohZgZMaglr3oG0687oUKTY2XHiCsOmbSMpzfa8sfCQQGaNboefpz5UEhERkeJB71ruwty5c2nXrh3nz5/nxIkThIaG0rVrV0JCQrh06RKrV68mNdW445GbmxsLFy7E39+/cJsWEbHFxQWaDYBGDxlDtjd8ACmXcq7PTIWo/8DOL6Dzn40B3e667bgIwNbjiYyeuYObmWab653rVWDqsDZ4e7g6uTMRERGR/NMZSHehRo0arF27ltDQUAAsFgvr1q1j+vTpfPfdd9nhUcWKFVmyZMltc5NERIokNw9oFwHP7YFur4GHX+71N6/CT6/DJ21gz1yw2P6DWaS0iDp6iVEzt+cYHnVvWInI4QqPREREpPhRgHSXGjZsyLZt25g1axa9evUiKCgIDw8PKlWqRFhYGB988AGHDh2iT58+hd2qiIj9PP2g21+MIKn9k+CSx4yW66dhyVPweSc4shKsVqe0KVKUrIu9yJhZO0nLtNhcv69xZT4f2hovd4VHIiIiUvyYrFa9yxfbwsPDiY6Ovu17YWFhbN26tZA6EpFCcyUe1r0P+xfaV1+zA9z7NgS1c2xfIkXETwcv8PTcXWSabb+t6tO8Kv8bGIq7qz67ExERkfwrzL/T9S5GRETyFhAM/SNhfBTU7Zl3/aktMP1emD8ELh1xfH8ihWjFvvNM+Crn8KhfaDUmKjwSERGRYk7vZERExH5Vm8PQb2DEMqjWKu/62OXwaRgsfQaun3V8fyJOtnTPWZ6dt4ssi+3waEDrGvzn8VDcFB6JiIhIMad3MyIicueCu0DEWnhsFgTWzb3WaoHds+HjVrDqTWPwtkgJsCjmDC8s2EMO2RFPtK/Jv/o3x9XF5NzGRERERBxAAZKIiOSPyQRN+sGEaOj7X/Crknt9VhpsnggTW8Cm/0HmTWd0KeIQ87af4uVFe3OcFz+yQ23e69cUF4VHIiIiUkIoQBIRkbvj6g5tRsNzu6D7X8GzbO71addh9VswqRXs+hLMWc7pU6SAfLn1BK9+uz/H8CiiczBvPdgYk0nhkYiIiJQcCpBERKRgePhClz/D83sh/Blw9ci9PukcfPcsfNYBDi8nx7/GRYqQaVFxvLn0YI7rE7rV4bXejRQeiYiISImjAElERAqWTwDc/x48GwMtngDy+EP68hFYMASm3wcntzilRZH8+HT9Mf6+4nCO6y/0rMfL9zdQeCQiIiIlkgIkERFxDP+a8Mhn8NRmqN8r7/oz22HGA/DV45CQ8xkeIoVh4uqj/GvlkRzXX76/AS/0rK/wSEREREosBUgiIuJYlZvAEwtg1A9Qo13e9Ud/hM86wuKn4Nopx/cnkgur1cqHPx7hv6t/zrHm9d6NePqePO5GKCIiIlLMKUASERHnqNUBxvwEg+ZChQZ5FFth71z4uDX8+DqkXnFKiyK/ZbVa+ccPsXyy7liONX97sDERXUKc2JWIiIhI4VCAJCIizmMyQcM+8NQWeOhjKFMt93pzBmz9BCa2gI3/howU5/QppZ7VauXtZYeYujEux5r3H2nGyI7BTuxKREREpPAoQBIREedzdYNWw+G5XXDvO+BVLvf69Buw9u8wqSXs/ALMmc7pU0oli8XKG0sOMHPLCZvrJhP8a0Bznmhf07mNiYiIiBQiBUgiIlJ43L2h4/Pw/F7jXzev3OuTE2D5izC5PRxcDFarc/qUUsNssfJ/3+7jq22252+5mOCjx1vweJsgJ3cmIiIiUrgUIImISOHzLm+cifTsLuPMJFMev56uHIevR0Jkd4jf6JQWpeTLMlv489d7WbjzjM11VxcTkwa35JGWNZzcmYiIiEjhU4AkIiJFR7nqxmykCdHQsG/e9ed2wawHYfajcH6f4/uTEivTbOGFBXtYvPuszXV3VxOTn2hF3+Z5zO0SERERKaEUIImISNFTsQEM+grGrIKaHfKuP74GpnSGb8bClXjH9yclSkaWhWfn7mb5vvM21z1cXfh8aGt6Na3i5M5EREREig4FSCIiUnQFtYNR38MTC6FS47zr938Nn7SF71+B5EuO70+KvfQsMxO+imHlwQs21z3dXIgc0YYejSo7uTMRERGRokUBkoiIFG0mE9S/H57cBP0+h3J5DC+2ZML2KTApFNb/E9KTnNKmFD9pmWYivoxh9eGLNte93V2ZMbItXetXdHJnIiIiIkWPAiQRESkeXFwhdDA8sxPuf98YvJ2bjGRY/w+Y1BK2TYWsDOf0KcVCakYWo2fuYOPPts9U8/VwZdbodnSoW8HJnYmIiIgUTQqQRESkeHH3gvCn4fm90PnP4Oade33KJfjhZZjcFvYvAovFOX1KkZWcnsXIGTvYcjzR5noZTze+HNOedsEBTu5MREREpOhSgCQiIsWTVzno8Vd4fg+0GQ0m19zrr56Ab8bA1K5wfK0zOpQi6EZaJsOnb2N7/BWb62W93Jgztj2ta+VxhpuIiIhIKaMASUREircyVaDvf+Hp7dC4X971F/bB7Edg1kNwdpfD25Oi43pqJsOmbWPXqWs218v7uDNvXBgtgvyd2peIiIhIcaAASURESoYKdeHxWRCxFmp3zrs+fgNE3gNfj4TE4w5vTwrXlZQMnpgWzd4z122uV/DzYP64cJpUK+fkzkRERESKBwVIIiJSslRvDSOWwdBvoEqzvOsPLobJ7WD5S5CU4Pj+xOkuJ6fzRGQ0B8/dsLleqYwn88eF0aBKGSd3JiIiIlJ8KEASEZGSx2SCuj1h3EZ4dBr418q93pIFO6fDpFBY+3dIsx00SPFz8UYag6ZGE3shyeZ61XJeLBgfTt1KCo9EREREcqMASURESi4XF2j+GDyzEx74F/jkcUv2zFTY+G+Y2AK2fgpZ6c7pUxzi/PWbDJwazbGLyTbXq/t7s2BcOMEVfJ3cmYiIiEjxowBJRERKPjcPaD/euGNb1/8D9zwCg5tX4MdX4eM2sHc+WMxOaVMKzpmrqQycEk385RSb6zUDfFgwPoyagT5O7kxERESkeFKAJCIipYdnGbjnVSNIajcOXNxzr79+ChaPhyld4OefwGp1Sptyd04lGuHRqSupNtdDKviycHw4NcorPBIRERGxlwIkEREpffwqQe9/wzPboemAvOsTDsDcx2BmXzi9w/H9Sb7FXUrm8SlbOXvtps31epX8mD8ujCrlvJzcmYiIiEjxpgBJRERKr4AQGDAdxm2AOt3zrj+5Cab3hPlD4NLPju9P7sixi0kMnBrNhRtpNtcbVinDvHFhVCqr8EhERETkTilAEhERqRYKwxbD8KVQNTTv+tjl8Gl7+O45uHHO0d2JHWIv3GDglGguJdkefN6kWlnmRYRRwc/TyZ2JiIiIlAwKkERERH4V0g0i1sGAGcbZSbmxWmDXLJjUEla9BTevOqVF+aMDZ68zeGo0iSkZNtdbBPkzd2wY5X09nNyZiIiISMmhAElEROS3XFyg6aPw9Hbo8xH4Vsq9PisNNv8PJobC5kmQafvyKXGMvaev8URkNFdTM22ut65Vnjlj2lHOJ4+B6SIiIiKSKwVIIiIitri6Q9sxxh3bur8BHmVyr0+7Bqv+Ch+3gt1zwGJ2RpelWszJKwydto0baVk219sFB/Dl6HaU8VJ4JCIiInK3FCCJiIjkxsMXurwMz++FsAngmsdlUDfOwtKn4bMOEPs9WK3O6bOU2RaXyLDp20lKtx0edawbyMxRbfH1dHNyZyIiIiIlkwIkERERe/gGQq9/wDM7ofkgwJR7/aVYmD8YvugFJ7c6pcXSYvOxy4yYsZ3UDNtneXWtX5HpI9ri46HwSERERKSgKEASERG5E+VrwaNT4MlNUO++vOtPR8OMXjB3ECQccnx/JdyGny8xeuYO0jItNtd7NqrE1OGt8XJ3dXJnIiIiIiWbAiQREZH8qNIUhnwNI1dA9TZ51//8A3zeEZZMgGunHd9fCbTmcAIRs3aSnmU7PHqgaRU+HdIaTzeFRyIiIiIFTQGSiIjI3ajdCcauhoFzILBe7rVWC+z5Cj5uDT++DqlXnNNjCbDywAWenBNDhtl2ePRgi2p8PLglHm56ayMiIiLiCHqXJSIicrdMJmj0IEyIhgcnQZmqudeb02HrJzAxFKL+AxmpTmmzuFq29xxPz91Fptn2QPJHW1bnfwNDcXPV2xoRERERR9E7LRERkYLi6gatR8Czu6Dn38CzXO716ddhzTvwcSvYOQPMtu8oVpot3n2G5+fvxmyxHR4NbBPEvx9rgatLHkPNRUREROSuKEASEREpaB4+0OlFeH4PdHgOXD1zr086D8tfgE/D4NBSsNoOS0qbhTtO89LCveSQHTE0rCb/eLSZwiMRERERJ1CAJCIi4ig+AXDfu/DcLmg5FEx5/NpNPAoLh8O0HhAf5Zwei6g50Sd55Zt9OWZpozrW5t2Hm+Ki8EhERETEKRQgiYiIOFq5GvDwZHhqCzTok3f92RiY1RfmDIAL+x3fXxEzY3M8byw5kOP6+K4hvNm3MSaTwiMRERERZ1GAJCIi4iyVGsHguTD6R6gZnnf9sVXweWf4dhxcPen4/oqAqRuP8/ayQzmuP9u9Lv/Xq6HCIxEREREnU4AkIiLibDXDYNQPMHg+VGyUR7EV9i2AT9rAD/8HKZed0mJh+GTtUd7/PjbH9Zfurc+f7mug8EhERESkEChAEhERKQwmEzR4AJ7aDA9/CmVr5F5vzoBtn8HEUNjwL0hPdkqbzmC1Wvlo1c98+NPPOdb8pVdDnutRz4ldiYiIiMhvKUASEREpTC6u0HIIPBsD9/0dvMvnXp+RBOveg0ktYXskmDOd06eDWK1W/vXjESatOZpjzRt9GvFUtzpO7EpEREREfk8BkoiISFHg7gUdnoXn9kCnl8DNO/f6lIvw/Z/hk7Zw4BuwWJzSZkGyWq28t+Iwn60/nmPNuw83YWznECd2JSIiIiK2KEASEREpSrz9oedb8NxuaD0STK6511+Nh0WjIbIbHF/nhAYLhsVi5W/fHWTapnib6yYT/PPRZgwLr+3cxkRERETEJgVIIiIiRVHZqvDgRHh6GzR6KO/683thdj/4sh+c2+Pg5u6OxWLl9SX7mbXV9p3lXEzw7wEtGNSuppM7ExEREZGcuBV2AyIiIpKLCvVg4Gw4sxNW/w1OROVeH7cOpq6Dpv2h+xsQYOflX1kZcGQFHFsN5/ZC4lHISgc3TwisB9VaQN2e0KAPuHnk+8cxW6z85Zt9LIo5Y3Pd1cXER4+34OHQ6vl+DREREREpeAqQREREioMabWDEMji2Bla/BQkHcq8/8A0cWgqtR0HXV8Cvku06cyZsnWw8Ui7+cT0rDRL2G4/dc8CvMoRNgPCnwdX9jn6ELLOFP329l6V7ztlcd3MxMWlwS3o3q3pHxxURERERx9MlbCIiIsWFyQT1esL4KHhkKvjncYmXJQt2RMLEUFj7HqTduH394mGY1sMIpH4bHplcoUIDqN7a+Pe3c5iSE4z6aT2M/XbKNFt4fv6eHMMjd1cTnw5ppfBIREREpIjSGUgiIiLFjYsLtBgITfrBzhmw8V+QmphzfWaKUbNzOnR5BdqMMuYkfTUA0n8JlUwu0LCvsRYUBh4+t/ZnpMLpaOO1YpeD1WLMXJp+HwxZBDXb59puepaZZ+buZtWhBJvrHm4uTBnWmnsa5HCWlIiIiIgUOgVIIiIixZWbJ4Q9CaFPwJaPjcvQMlNyrk9NhJV/gc3/hZvXjMvTACo2hH6fGmcc2eLhA3W6G4+zMbBkAlyKNcKnrwbAmFVQqaHNrWmZZp6aE8O6I5dsrnu5uzBteFs61atwBz+4iIiIiDibLmETEREp7rzKQvfX4fk90DYCXPL4fCjpwq3wKKQbRKzNOTz6veqtjfqQbsbX6Tdg8ThjltLv3MwwE/HlzhzDIx8PV2aMbKfwSERERKQYUIAkIiJSUvhVgj4fwtPbjbuw5aViQxg0Fzx87+x1PHyNfRV/Oevo/F7j7KffSEnPYtTM7UQdvWy7VU83vhzdjvA6gXf22iIiIiJSKBQgiYiIlDSBdWDAFzBu/a0zhX7P5GJctnan4dGvPHzh4U+N4wBEf5p9FlJSWiYjZ2wnOu6Kza1lvNyYPaYdbWoH5O+1RURERMTpFCCJiIiUVNVawvClMGwxVG1x+1rDvvZftpaTGq2hYR/jeXICxC7n+s1Mhn+xnR0nrtrc4u/jztyxYbSsWf7uXltEREREnEoBkoiISElXpztErIfanW59r82ogjl2m9HZT9NjVzF02jZ2n7pmszTA14O5Y8NoVqNcwby2iIiIiDiNAiQREZHSwMUFbl43nptcoWZ4wRw3KCz7MrYzh7ay/+x1m2UV/DyZPy6MxtXKFszrioiIiIhTKUASEREpLRKPGv8G1gV374I5poePcTygWtYZmyWVy3qyYHwY9SuXKZjXFBERERGny+M+vyIiIlJiZKUb/3r6FexxPYzjeZL5h6Vq5byYGxFG7Qr5HNYtIiIiIkWCzkASEREpLdw8jX/Tkwv2uBnG8dJxv+3bNcp7s2B8uMIjERERkRJAAZKIiEhpEVjP+DfxGGTeLJhjZqQaxwPirFWzv1070IeF48MJCvApmNcRERERkUKlAElERKS0qNbC+NdqhlNbC+aYp6PBagFgvyUYgJCKviwYH041/wKasyQiIiIihU4BkoiISGlRt+et5ztnFMwxd36R/bSF6Rj3V7jEgnHhVC7rVTDHFxEREZEiQQGSiIhIadGgD/hVNp7HLoezMXd3vDMxELsi+8tGrmf4POVFKq5/BZIv3d2xRURERKRIUYAkIiJSWrh5QNgE47nVAksmQEZK/o6VkQJLJ2RfvvYrk9UCMTPh41aw6X+37vwmIiIiIsWaAiQREZHSJPxpqPrLLKRLsTD/iTsPkTJSjH2XYnOuSb8Bq9+Cye3g0FKwWvPfs4iIiIgUOgVIIiIipYmrOzwyBTzLGl/HrYfI7sblaPY4E2PUx60HwIop9/qrJ2DhcJjZB87tyWfTIiIiIlLYFCCJiIiUNpUawZBFt0KkS7EwvScsGArH10JG6u31GanG9xcMNep+OfMoxeRL2qBF0OklcPXM/TVPboap3WDJ05B0oeB/JhERERFxKLfCbkBERESc593lh1ix7zwAta3v8jfTRBpa44xZRoeXGQ+TCwTWBQ8/yEiGxGN/mHV0wBrM392f58S3JqATQxp34lnLbDi4OJdXt8KeOUZN5xch/Blw93bcDysiIiIiBUZnIImIiJQSVquVlQcucOFGGhdupBGdVJG+N//GPzIHc9Hq/5tCC1z+Gc7tMv79TXh00erPPzIH0y/9baKTKmUfa+FxEzw2E0athGotc28kMwXW/h0+aQv7F2k+koiIiEgxoDOQRERESomrqZlcSrr9rmhZuDHN+hDLPR6hq3U7YZbdNDXFU9t0DpesdCxunpywVuOANZhol5ZsMLXjQqYZM7eHPgnX07mSkkFArXAYuxb2LYA1b0PS+Zwbun4avhkD26fC/f+AGq0d8WOLiIiISAFQgCQiIlJKBPh6sPqlrvzjh8P8cODWHCKzxUrzWhV48oE/UTPQ57Y9LkDIL4/QxFSufH+Yswdvn2HUu1kVXn2gEQG+Hr9scoHQwdD4Idg8CTZPhKybOTd2ehtM6w7NB0KPt6Bc9YL5gUVERESkwOgSNhERkVKkZqAPnw1tzX8HhlLexz37+z8cuEDPjzaw48QVm/t2nLhCz482sPI34VHjqmWZPy6MT4e0JijA54+bPHzhnlfh2Z3Q7PG8m9u3AD5uDev+ARkpd/yziYiIiIjjKEASEREpRWJOXmH87J28tHAPV1Mzb1vLMFuYv/20zX3ztp8iw2zMQqrg58kH/Zux7NlOhIUE5v2i5WpA/0gYuwZqtM29NusmbPgnfNwG9s4HiyX3ehERERFxCl3CJiIiUsKZLVZWHbrA1I1x7Dp1Lddad1eT7e+73PrM6S+9GvBYm6A7b6RGGxizCg58A6veghtncq5NOgeLx8O2KdDrH1Az7M5fT0REREQKjM5AEhERKaFuZpiZvfUE3f+znifn7MozPAIY0LoGWWYLn284zksL9/D5huNkmS0MaFMju2bpnnP5b8pkgmYDjMvaur8B7r6515/bBV/cD1+PhKsn8/+6IiIiInJXdAaSiIhICXMpKZ3ZW08wO/rkHy5Ty03tQB8ql/Vi4NRoYk5ezf7+6kMJ/HdgKLUCfTiZmMrm45c5e+0m1f2989+kuzd0eRlCh8Lav8Oer+B3d3a7zcHFEPs9dHgGOr0InmXy/9oiIiIicsd0BpKIiEgJcexiMq9+u4+OH6xl0tpjdoVHrqZbl6yV9Xan98So28IjgJ0nr9J7YhRlvYyh21YrLN6Vy+Vnd6JsVeg3Gcath1odc681p0PUf4xB27tmg8VcMD2IiIiISJ4UIImIiBRjVquV7fFXGDtrBz0/2sC87afJyMp78LS/jzvP3FOHqv5e2d/bd+Y6SelZAAQFePNB/2YEBRhnGSWlZ7H/7PXs2kUxZ7Baczlj6E5VC4WRK+DxL8G/Vu61yQnw3TMwtSvERxVcDyIiIiKSI13CJiIiUgxlmS38eDCBqVFx7D19ze59QQHejO0UwmNtanDw3A0+WXf8DzWPtqzO2w83oYyXO72bVeXNpQdZvPvsbTUnElOJOXmVNrUD7vZHucVkgsYPQ737YdvnsPFDyEjKuf7CfpjVFxr2hfvehYCQgutFRERERG6jAElERKQYSUnP4uudp5m+OZ7TV27ava9FkD/ju4Rwf5MquLoYl60t2nn7ZWhlPN34+yNNeTi0+q3vebnz34GhdGtQkTcWH8g+QwmMs5AKNED6lbsXdHoBQp+Ade/Bri/BmstZVbHL4ehP0P5J6PJn8CpX8D2JiIiIlHIKkERERIqBizfSmLX1BHOiT3H9pn2DsU0m6NmoMuO6hNCmVnlMv5l3BPBwaDXWxCZwOTmDNrXK89+BoQQF+Ng81sOh1WlVszwvLtjDzpNXqeDnwUMtqt31z5Urv0rw4ERoGwE/vgbxG3KuNWfAlkmwZy7c8xq0GgGuepsjIiIiUlD0zkpERKQIO5qQRGRUHEt2nyPDnPdsIwAPNxcGtK7BmE7B1Knol2Ndh7oVWPFcZ1YeuMCQ9jVxc819NGJQgA/zx4Xx1bZT9GpahcplvXKtLzBVmsLwpXDkB/jpDbjyx8vusqVehhUvwY5pcP97UKe7c3oUERERKeEUIImIiBQxVquV6LgrREbFsTb2ot37yvu4Mzy8NsPCa1HBz9OuPZXLejGiQ227X8PN1eWO6guMyQQNe0PdnrAjEtZ/AOnXc66/eAhmPwL1e8F9f4cK9ZzXq4iIiEgJpABJRESkiMgyW/j+wAUiN8bddsezvNQO9GFs5xD6t6qBt4erAzssAtw8IPxpaD4I1v8Ddn4BVnPO9T+vhGOrod046PoKeJd3Xq8iIiIiJYgCJBERkUKWnJ7Fgh2n+WJTPGev2T8Yu3Wt8kR0DuHexpWzB2OXGr6B0OdDaDsWfnrdCIlyYsmC6E9h7zzo9iq0GQ2u7s7rVURERKQEUIAkIiJSSC5cT2PmlhN8te0kSWlZeW/AuJLr/sZViOgSTOtaDrgDWnFTqSEM/QaOroIfX4fLR3KuvXkVfnjll/lI70O9e53Xp4iIiEgxpwBJRETEyWIv3CByYzzf7T1Lptlq1x4vdxceax3EmE7B1K7g6+AOi6F690JIN9g5A9a/b4RFObn8M3w1AOr0MAZtV2rktDZFREREiisFSCIiIk5gtVrZfCyRqVFxbPz5kt37An09GNGhNkPDahHg6+HADksAV3doPw6aPwYb/gXbpxqXr+Xk+Br4bD20GQXdXjMuixMRERERmxQgiYiIOFCm2cLyfeeYujGew+dv2L0vpKIvEZ1DeKRldbzcS/hg7ILmXR56/QPajIGf3oCff8i51mo2Lmnb97UxZLvdOGNQt4iIiIjcRgGSiIiIAySlZTJ/+2m+2BzP+etpdu9rFxzAuM4hdG9YCZfSNhi7oFWoC0/Mh+Pr4MfX4OKhnGvTrxvDuHdOh/v+Dg16GwOnRERERARQgCQiIlKgzl27ycwtJ5i37RRJ6fYNxnYxwQNNqzK2czAta+o28wWuzj0wPgp2fwlr34PUyznXXomD+U9AcBe4/x9Qpanz+hQREREpwhQgiYiIFICD564zLSqeZXvPkWWxbzC2t7srA9sGMbpjMDUDfRzcYSnn6gZtRkPT/hD1H4j+DMwZOdfHb4QpnaHlMOj+BvhVcl6vIiIiIkWQAiQREZF8slqtbDx6mciNcWw6lstZLb9Twc+TUR1rM6R9Tfx9NG/HqbzKwb3vQOuRsOpNOLws51qrBXbNggPfQpc/QfunwN3Laa2KiIiIFCUKkERERO5QRpaF7/aeY1pUHLEXkuzeV7eSH+M6h/Bwy2p4umkwdqEKCIGBc+DEJlj5KlzYl3NtRhKs/hvsnGGET40f1nwkERERKXUUIImIiNjp+s1M5m0/xYzN8STcSLd7X3hIIOO6hNC1fkUNxi5qaneCceth7zxY8w4kJ+Rce+0kfD0CanaAXu9DtZZOa1NERESksClAEhERycOZq6nM2HyC+dtPkZJhtmuPq4uJ3s2qMq5zCM1qlHNwh3JXXFyh5VDjzKJN/4MtH4M5l4Dw1BaYeg+EPgHd/wplqzqtVREREZHCogBJREQkBwfOXmfqxjhW7D+P2c7B2D4ergxqW5NRHWsTFKDB2MWKZxno8VdoPQJWvQUHv82l2Ap7voKDS6DTi9DhGXD3dlanIiIiIk6nAElEROQ3LBYrG36+xNSNcWyNS7R7X6UynozqGMwT7WpSzsfdgR2Kw/nXhMdmQPsn4cdX4WxMzrWZKbDu7xAzE+5927jLm+YjiYiISAmkAElERARIzzKzdPc5IqPiOHox2e59DSqXIaJLCA+1qIaHm4sDOxSnq9kexqyG/V8bQ7STzuVce+MMfDMGtn0O9/8Dgto6rU0RERERZ1CAJCIipdq11Ay+2naKmVtOcCnJ/sHYnepWIKJLCF3qVcCkM05KLhcXaDEQGvU1ZiNt+h9k3cy5/swOmN4Tmj0GPf8G5Wo4q1MRERERh1KAJCIipdLpK6lM3xTPwp2nSbVzMLabi4kHW1RjbOdgmlTTYOxSxcMXuv0ftBxm3K1t3/zc6/d/DYeXQ8fnoOPzxn4RERGRYkwBkoiIlCp7T19jalQcP+w/j51zsfHzdGNwuyBGdQymmr8GJZdq5arDo1Og3ThjPtLpbTnXZt2EDR/Ari+hx5vQfJBxRpOIiIhIMaQASURESjyLxcra2ItMjYpje/wVu/dVKevF6E61GdSuJmW9NBhbfqNGaxj9o3GntlV/g+uncq5NOg9LnoLtU435SLXCndamiIiISEFRgCQiIiVWWqaZxbvPEhkVR9ylFLv3NapalnFdgunTTIOxJRcmk3HXtQa9YetkiPrIuCtbTs7thhm9oHE/445t5Ws7q1MRERGRu6YASURESpwrKRnMiT7Jl1tPcDk5w+59XepXZFznEDrWDdRgbLGfuzd0+TO0HApr34XdXwG5XB95aAkc+QHCn4bOL4FnGWd1KiIiIpJvCpBERKTEOJmYwrSoeL6OOU1apsWuPe6uJh5qUZ2xnYNpVLWsgzuUEq1MFXh4sjEfaeVrcHJTzrXmdNj0EeyeA93fMMInF1fn9SoiIiJyhxQgiYhIsbfr1FUiN8ax8uAFrHYOxi7j5caQ9rUY2aE2Vcp5ObZBKV2qtoCRy+HwMlj1V7h6IufalIuw7DnYHgm93ofgLk5rU0REROROKEASEZFiyWyxsvpwApEb49h58qrd+6r7ezOqozEY289TvwbFQUwmaPwQ1L8ftn0OGz+E9Bs51yfsh1kPQsO+cO87EFjHeb2KiIiI2EHvnEVEpFhJyzSzKOYM0zfFE3/Z/sHYTauXJaJzCL2bVcXdVYOxxUncPKHj89DiCVj3HuyaBdZcLq+MXQ4//wjtx0OXl8Hb32mtioiIiORGAZKIiBQLicnpfLn1JLOjT3Ilxf7B2Pc0qEhElxDCQzQYWwqRX0V48H/QLgJ+fA3i1udca8mErZ/A3nlwz2vQaiS46i2biIiIFC69GxERkSIt7lIy0zbF803MGdKz7BuM7eHqQr+W1RjbOYT6lXWHKylCKjeBYUuMs4x+eh0Sj+Vcm5oIK/4E26fB/e9B3R5Oa1NERETk9xQgiYhIkWO1Wok5eZWpG+NYdTjB7sHY5bzdGRpWkxHhtalUVoOxpYgymaBBL6jTHXZMgw3/hLTrOddfOgxzHoV698N9f4eK9Z3Xq4iIiMgvFCCJiEiRYbZY+engBaZGxbH71DW799Uo783YTsE81iYIXw3GluLCzQPCJ0CLQbD+n0aYZDXnXH/0Rzi+BtqOha5/AZ8A5/UqIiIipZ7eZReAjIwMFixYwLx58zh48CAJCQmUL1+e4OBgHn30UUaOHEmFChUK9DUPHz7MqlWr2LRpEwcOHODs2bOkpqZSrlw5ateuTceOHRk5ciQtW7Ys0NcVEXGE1IwsFsWcYVpUPKeupNq9r0WNckR0CaFXkyq4aTC2FFc+AdD7X9B2DPz4OhxblXOtJcu4q9ve+dDtVWOPq7vzehUREZFSy2S12nthgNgSGxvLE088we7du3OsqVSpEjNmzKB37953/XqrVq3ixRdf5ODBg3bVP/bYY3z++ecEBNz5p5Th4eFER0ff9r2wsDC2bt16x8cSEbHlUlI6X249wezok1xLzbR7X89GlYjoHEK74AANxpaS5+hqYz7Spdi8awPrGfOR6t1nXBonIiIiJVph/p2uM5DuwpkzZ+jRowfnzp0DwGQy0aVLF+rWrcvFixdZvXo1N2/e5OLFi/Tr148ffviBHj3ubgBmTEzMbeGRyWSiefPm1K9fn/Lly3Pp0iU2bdrEpUuXAPj66685dOgQGzZsIDAw8K5eW0SkoBy7mMS0qHi+3X2WDHsHY7u50L9VdcZ0CqFuJT8HdyhSiOr1hJBuEDMD1r0PN6/kXJt4FOY+bsxTuu89qNzYaW2KiIhI6aIA6S4MGTIkOzyqVasW3333Hc2bN89ev3z5MoMGDWLNmjVkZmby+OOPc/z4cfz9/e/6tUNDQ4mIiGDgwIF/CIYyMjL46KOPeOONNzCbzRw8eJAJEyawYMGCu35dEZH8slqtbIu/QuTGONbEXrR7n7+PO8PDajEsvDYVy3g6sEORIsTVDdpFQLMBsPFD47I1S1bO9cfXwucdofUouOc18C3YS+dFREREdAlbPn3//ff06dMHAA8PD3bu3EmzZs3+UJeSkkLz5s2Ji4sD4NVXX+X999/P9+t+++23uLi40K9fvzxr//vf//LSSy9lf3348GEaNmxo92vpEjYRKQhZZgsrD14gcmMce8/kcqep36kV6MPYTsH0b10DHw993iGl3OVjsOqvcOT7vGs9y0HXl6HdeGNQt4iIiJQYhfl3uiaO5tPkyZOzn48YMcJmeATg6+vLO++8k/31lClTyMrK5RPEPDz66KN2hUcAzz33HNWqVcv++vvv7XjTKSJSQFLSs5ixOZ5uH67nmbm77Q6PWtb05/OhrVj7p24MC6+t8EgEoEJdGDwPhi+Fyk1zr02/Dj+9AZ+2h8PLQZ8VioiISAFQgJQPycnJrFmzJvvrUaNG5Vo/YMAAypQpA8CVK1fYuHGjQ/v7laurK+3bt8/++sSJE055XREp3S7eSONfK2MJ/8ca3l52iDNXb+a5x2SC+xpXZtGT4Sye0JFeTavi6qKBwCJ/ENINxm+EByeCb8Xca6/EwYIhMOtBOL/PKe2JiIhIyaWPdfNhy5YtpKenA8YZRm3bts213tPTk7CwMFatMm7Lu3btWrp37+7wPoHb7k5kNpud8poiUjr9nJBE5MY4lu45R4bZvsHYnm4uDGhdgzGdggmpqMHYInZxcYXWI6HJoxD1H4j+FMwZOdefiIIpXaDVMLjnDShT2WmtioiISMmhACkfDh8+nP28WbNmuLnl/T9jq1atsgOk3+53tP3792c/DwoKctrrikjpYLVa2Xo8kalRcaw/csnufQG+HgwPr8WwsFoE+mkwtki+eJWFe982wqTVb8GhpbkUW2HXl3DgW+j8JwibAO5ezupURERESgAFSPlw5MiR7Oe1atWya0/NmjWzn8fGxhZ4T7Zs2bKFo0ePZn/ds2fPuz7mgQMHCA8Pz9deDd8WKTkyzRa+33+eyKg4Dpy9Yfe+4Aq+jO0cTP9WNfByd3VghyKlSEAwPP4lnNgMP74K5/fmXJuRDGvehpgZcO870LifcQ2piIiIOFV+/64+cOBAAXdiPwVI+ZCYmJj9vHJl+04Dr1KlSvbzK1euFHhPv2exWHjxxRezv27fvj1t2rS56+MmJyf/YeK7iJQeyelZzN9+ihmbT3D2Wt6zjX7VtnZ5IjqH0LNRZVw020jEMWp3hIj1sHcerHkHki/kXHvtFHw9EmqGw/3vQ/VWzupSREREoFj+Xa0AKR+Sk5Ozn3t7e9u157d1v93vKO+++y7bt28HwMXFhQ8//NDhrykiJdeF62nM2BLP3G2nSEqz706SJhP0alKFiC4htKpZ3sEdiggALi7Qcgg0fhg2/w+2fAxZaTnXn9oKkfdAi8HQ400oWy3nWhERESnVFCDlQ1rarTdiHh4edu3x9Lw14+PmTfs/tc+PZcuW8fbbb2d//fLLL9OpUyeHvqaIlEyHz98gMiqO7/acI8ti363AvdxdeLxNEGM6BVMr0NfBHYqITZ5+0P0NaDUCVv8NDizKvX7vPGOGUqcXIfwZ8PBxSpsiIiJSfChAygcvr1tDJzMycrnryW/8etc2sP+spfzYsWMHgwcPxmo1/tDr0aMH7777rsNeT0RKHqvVyqZjl5m6MY6oo5ft3lfBz4MR4bUZGlaL8r72hesi4mD+QTBgOrQfDytfhbM7c67NTIV170HMTOj5N2g6wDijSURERAQFSPni53frVtP2nk3027rf7i9Ihw4d4oEHHiAlJQWAtm3bsmTJEtzd3QvsNfz8/GjatGmBHU9Eio5Ms4Xl+84xdWM8h8/bPxi7TkVfIjqH0K9ldQ3GFimqgtrBmFXGmUir/wY3zuZce+MsfBsB26ZAr39CUFuntSkiIlJahIWF5WvfgQMHnDIWxxYFSPkQGBiY/TwhIcGuPRcu3BpkGRAQUOA9xcfHc++992YP+G7cuDE//PBDgYdVTZs21d3UREqYG2mZzN9+ii82neDCjVxmpfxO++AAxnUJ4Z4GlTQYW6Q4cHGB5o9Dw77GbKTN/zPOOsrJ2Z0wvadxJlLPvxlnM4mIiEiByO/f1eHh4YU2gFsBUj40aNAg+/nJkyft2nPq1Kns5w0bNizQfs6ePUuPHj04d+4cAHXq1GHVqlW3BV0iIr937tpNZmyOZ9720ySn2zcY28UEvZtVJaJzCC2C/B3boIg4hocPdPsLtBpm3K1t77zc6w8sgtjl0OFZ6PiCMV9JRERESh0FSPnQqFGj7Of79+8nKysLN7fc/6fctWuXzf136+LFi/To0YP4+HgAatSowerVq6lWTXdRERHbDpy9zrSoOJbvO2/3YGwfD1cGtg1idMdgggI0XFekRChbDR75HNpFwMrX4HQun2ZmpcHGf8Ou2cbd2loM1nwkERGRUkYBUj506NABT09P0tPTSUlJYefOnblev5ienn7bKWbdu3cvkD4SExPp2bMnR44cAaBSpUqsXr2a2rVrF8jxRaTksFqtbPj5EpFRcWw+lmj3voplPBnZoTZD2tfE30eDsUVKpOqtYfRKOLgYVr0F10/lXJt8AZZOgO1Todc/oFYH5/UpIiIihUoBUj74+fnRo0cPvv/+ewBmzpyZa4D07bffkpSUBED58uXp0qXLXfdw48YNevXqxf79+7OPu2rVqtsurxMRSc8y892ec0yLiudIQpLd++pV8iOiSwgPh1bD002DsUVKPJMJmj4KDXpD9GSI+ggychnQeX4PzHgAGj8M974D5Ws7q1MREREpJDr3OJ8mTJiQ/XzGjBkcPHjQZl1qaipvvvlm9tfjx4/P83K3vKSmptKnTx927jRuxVumTBlWrlxJ8+bN7+q4IlJyXL+Zyafrj9H5g3W8vGif3eFRhzqBzBjVlp9e7MLjbYIUHomUNu5e0PlP8OwuaDkMyGNA/qGl8Elb48ylNPvv3igiIiLFjwKkfOrTpw+dO3cGICMjg759+2afDfSrxMRE+vXrx7FjxwDj7mt/+ctfbB7vxIkTmEym7Mf69ett1qWnp9OvXz82bdoEgLe3N8uXL6ddu3YF9JOJSHF2+koq7yw7RId/rOFfK49wMSk9zz2uLiYeDq3G8mc7MTcijHsaVMJk0l3VREq1MpXh4U9g/Eao3Tn3WnOGcUe3j1tBzEywmJ3RoYiIiDiZLmG7C3PnzqVdu3acP3+eEydOEBoaSteuXQkJCeHSpUusXr2a1FTj9rhubm4sXLgQf3//u3rNv/71r6xatSr760aNGrFw4UIWLlyY59569erx/PPP39Xri0jRtO/MNSKj4vl+/3nMdg7G9vVwZXC7mozqFEx1f28HdygixVLV5jBiGcSugJ/egKvxOdemXIJlz8P2SLj/fQjp6rw+RURExOEUIN2FGjVqsHbtWgYPHsyePXuwWCysW7eOdevW3VZXsWJFZsyYQY8ePe76NS9evHjb17t27brtDm+56dq1qwIkkRLEYrGy/ueLTN0YR3TcFbv3VS7ryaiOwQxuV5Ny3u4O7FBESgSTCRr1hXr3GsOzN/wL0nO5XC3hAHz5EDToA/e9C4F1nNeriIiIOIwCpLvUsGFDtm3bxvz585k3bx4HDx4kISEBf39/QkJCeOSRRxg9ejQVKlQo7FZFpIRIyzSzdM9ZIqPiOXYxlyG3v9OwShkiOofwYItqeLjpCmYRuUNuntDhWWgxGNa9DzEzwGrJuf7ICjj6E7QfD11eBm9/p7UqIiIiBc9ktVrtu9ZBSp3w8HCio6Nv+15YWBhbt24tpI5ESrdrqRnMiT7JzC0nuZyc92yjX3WuV4GIziF0rldBs41EpOAkHIIfX4O4dXnXegfAPa9B61Hgqs8vRURE8qsw/07Xb3ARkSLuVGIqX2yOZ8GO09zMtG84rZuLiYdaVGNs5xAaVyvr4A5FpFSq3BiGLTbOMvrxdUg8mnPtzSvw/Z9hxzS4/z2o29N5fYqIiEiBUIAkIlJE7Tl9jciNcfxw4Dx2zsWmjKcbT7SvyciOtalaToOxRcTBTCaofz/U6Q47psP6f0DatZzrL8XCnP5Q7z647+9QsYHTWhUREZG7owBJRKQIsVisrIm9SOTGOLafsH8wdrVyXozuFMzAtkGU8dJgbBFxMld3CHsSmj8OGz4w7sRmzeWMyaM/wbE10HYsdPs/8AlwXq8iIiKSLwqQRESKgLRMM9/uOsu0qDjiLqfYva9x1bKM6xJCn+ZVcXfVYGwRKWQ+AfDAB9BmDPz0Bhz9Medaqxm2T4F9C4wQqe1YI4gSERGRIkkBkohIIbqSksHsrSf5cusJElMy7N7XtX5FxnUJoUOdQA3GFpGip2J9GLLQOMvox9fh0uGca9Ouwcr/M+Yj3feecUlcXv9dy8ow7vJ2bDWc22vMX8pKN+4UF1gPqrUw5iw16ANuHgX6o4mIiJRWCpBERApB/OUUpm+KY1HMGdIyc7kN9m+4u5p4OLQ6YzsH07CKBmOLSDFQtwcEd4VdM2Hte8Yw7ZwkHoN5AyGkG9z/PlRu8scacyZsnWw8Ui7+cT0rDRL2G4/dc8CvMoRNgPCndXaTiIjIXVKAJCLiRDEnrzJ143F+OpSA1d7B2F5uDA2rxcgOtalc1suxDYqIFDRXN+PytKYDYOO/YdsUsGTmXB+3Hj7vBK1Hwj2vg28F4/sXD8Pi8XB+7+31JlcIrAuefpCebARRv85fSk6A1W/BwW/hkSlQqZEjfkIREZFSQQGSiIiDmS1WVh1KIDIqjpiTV+3eV93fmzGdgnm8bRB+nvrPtYgUc97+cP970GY0rHoTYpfnXGu1wM4vYP8i6PIyVGsJ85+A9BvGuskFGvaFNqMgKAw8fG7tzUiF09Gwc4bxGlaLETpNvw+GLIKa7R36Y4qIiJRU+otERMRBbmaYWbTrDNOj4jiRmGr3vmbVyzGuSwgPNK2CmwZji0hJE1gHBn0F8Rth5WvG5WY5Sb8Bq/4KmIBfTtus2BD6fQrVW9ve4+EDdbobj7MxsGQCXIo1jvXVABizCio1LOifSkREpMRTgCQiUsAuJ6fz5daTzN56gqupuVym8TvdG1YionMIYSEBGowtIiVfcBcYv8GYVbT2XUi5lEvxL+FRSDcYNBc8fO17jeqtIWKtcfZS3HojRFo8Dsau0UwkERGRO6QASUSkgBy/lMy0qHi+2XWGjCz7BmN7uLrwSEtjMHa9ymUc3KGISBHj4gqtR0CTR2DTR7D1UzCn266t2PDOwqNfefga+yK7G2cind9rDOHu9MJdty8iIlKaKEASEbkLVquVHSeuMnVjHKsPJ9i9r5y3O8PCajG8Qy0qldFgbBEp5bzKQs+/GYOzV70Fh5bcvm5yMS5bu9Pw6FcevvDwpzC9pzETKfpT3ZlNRETkDilAEhHJB7PFyo8HLzB1Yxx7Tl+ze19QgDdjO4XwWJsa+HjoP8EiIrcpXxsenwUnt8Dip+DaCeP7DfvmPPPIXjVaQ8M+cHiZcXe22OXGmU8iIiJiF/31IiJyB1Izsvh65xmmbYrj9JWbdu9rEeTP+C4h3N+kCq4umm8kIpKrWh2gdifYc8L4us2ogjlum9FGgARwbI0CJBERkTugAElExA4Xk9L4cstJZkef5PpN+wdj92xUmfFdQ2hTq7wGY4uI3Inze41/Ta5QM7xgjhkUZlwOZ7XA+T0Fc0wREZFSQgGSiEgujl1MInJjPIt3nyXDbOdgbDcX+reqwdjOwdSp6OfgDkVESqjEo8a/gXXB3btgjunhYxzv8s9w+VjBHFNERKSUUIAkIvI7VquVbfFXiNwYx5rYi3bvK+/jzrDw2gwPr0UFP08HdigiUgpk/XI3Ns8CDuI9fjleVlrBHldERKSEU4AkIvKLLLOFHw5cIDIqjn1nrtu9r3agD2M6hzCgVQ28PVwd2KGISCni5mmEPOnJBXvcjF+O56Y7YIqIiNwJBUgiUuolp2excMdppm+K5+w1+wdjt65VnojOIdzbuLIGY4uIFLTAepCwHxKPQebNgrmMLSPVOB5AuRp3fzwREZFSRAGSiJRaCTfSmLnlBF9Fn+RGWpZde0wmuL9xFSK6BNO6VoCDOxQRKcWqtTACJKsZTm2FOt3v/pino40B2mDMWPryYejwnHFs3ehAREQkVwqQRKTUOXIhicioOJbuOUum2WrXHi93Fx5rHcToTsEEV/B1cIciIkLdnrB7jvF854yCCZB2fnH713HrjUflZtDxOWjyCLi63/3riIiIlEAKkESkVLBarWw5nsjUjXFs+PmS3fsCfT0YHl6bYeG1CPD1cGCHIiJymwZ9wK8yJCdA7HI4GwPVW+f/eGdiIHaF7bWE/fBtBKx5B8KeglbDwbNM/l9LRESkBFKAJCIlWqbZwvf7zzN1YxwHz92we19IBV/Gdg7h0VbV8XLXYGwREadz84CwCbD6LeOysyUTIGIteOTjLNCMFFg64dblazm5fhp+fA02fABtxkD7J6FM5fz1LyIiUsIoQBKREikpLZMFO07zxaZ4zl23/1bN7WoHENElhB4NK+GiwdgiIoUr/Gk4+C2c3wuXYmH+EzBo7p2FSBkpxr5LscbXJpe8g6S067DpI9j6CbQYBOHPQsX6+f85RERESgAFSCJSopy/fpOZm08wd9spktLtG4ztYoIHmlZlbOdgWtYs7+AORUTEbq7u8MgUmH4fpN8w5hVFdoeHP4UadlzOdibGOPPo1/DIsxwMXQQnN0P0Z8blcbkxZ8CuL41Hgz7GnKSaYXf9Y4mIiBRHCpBEpEQ4dO4G06Li+G7vObIs9g3G9nZ3ZWDbIEZ3DKZmoI+DOxQRkXyp1AiGLIKvBhgh0qVYmN4TGvaBNqMhKAw8fvPf8IxU425rO78wZh79eraRZzkY8jUEtTMeYRNg30LY8jFcPpJ3H0dWGI8a7aDj89CgN7i4OOZnFhERKYIUIIlIsWW1Wok6epnIqDiijl62e18FP09GdqjF0LBa+PtoMLaISJFXsz2MWQWLxxmXs1ktcHiZ8TC5QGBd8PCDjGRIPPbHS9SqtoBHpkKlhre+5+YJrYZB6BA4+hNsngintuTdy5ntsGCI8Zrhz0CLweDuVbA/r4iISBGkAElEip2MLAvL9p4jMiqO2AtJdu+rW8mPiM7BPByqwdgiIsVOpYYwdg1snQzRn966/Mxqgcs/297jV9k40yj8aeNyOFtcXKBBL+NxegdsmQiHlwN5nM2aeAyWvwDr3of244yh2z4B+f3pREREijwFSCJSbNxIy2TetlPM2HyCCzfsH4wdFhLAuC4hdKuvwdgiIsWaqzt0esEIhGKXw7E1cH4PXD4GWWng5gUV6kLVUKjbAxr2zTk4siWoLQycA4nHjQHau78Cc3rue1Iuwtq/Q9R/odVwCJ8A/jXv4ocUEREpmhQgiUiRd/baTWZsimf+jtMk2zkY29XFRO9mVYnoHEzzGv6ObVBERJzL1R2aPGI8HCGwDvT9L3R7DbZPhR2RcPNq7nsyU2DbZ0Z900ehw3NQtblj+hMRESkECpBEpMg6cPY6kVFxLN93HrOdg7F9PFwZ1LYmozrWJihAg7FFROQu+FWE7q8bZz3tnmOclXTtVO57rGbY/7XxCOlmBEl1uoNJZ8CKiEjxpgBJRIoUq9XK+p8vEbkxji3HE+3eV6mMJ6M6BvNEu5qU87mDyxVERETy4uEL7ccbc44OLzUGbp/fm/e+uPXGo3Iz6PicccbUnVxSJyIiUoQoQBKRIiE9y8zSPeeYFhXHzwnJdu+rX9mPiM4hPBRaDU83DcYWEREHcnWDpv2hyaMQvxG2TIJjq/Pel7Afvo2ANe8YQ71bDQdPP8f3KyIiUoAUIIlIobqemsmcbSeZueUEl5LyGFT6Gx3rBhLROYSu9Sti0mUBIiLiTCYThHQ1HhcOwJaP4cAisOQxp+/6afjxVdjwAbQdA+3GQ5nKzulZRETkLilAEpFCcfpKKl9sjmfBjtOkZpjt2uPqYuLB5lUZ2zmEptXLObhDERERO1RpCo9OgR5/hejPIGYmZORxJm3aNYj6jxE8tRgMHZ6FCvWc0a2IiEi+KUASEafad+YaUzfG8f3+89g5Fxs/TzcGtwtiZMdgqvt7O7ZBERGR/ChXA+5/D7q8DDu/gG2fQ3JC7nvMGbBrFuz6Ehr0NuYk1QxzTr8iIiJ3SAGSiDicxWJl3ZGLTN0Yx7b4K3bvq1LWi9GdajOoXU3KemnoqIiIFAPe/tD5JQh/GvYtNOYkXf45j01WOLLCeAS1N+7c1qA3uLg4o2MRERG7KEASEYdJyzSzZPdZIqPiOH4pxe59jaqWZVyXYPo0q4aHm948i4hIMeTmCa2GQegQOPojbJ4Ep7bkve/0NlgwBALrQYdnoPkgcPdyfL8iIiJ5UIAkIgXuakoGc6JPMmvrCS4nZ9i9r3O9CozrEkKnuhU0GFtEREoGFxdo8IDxOL0DtkyEw8uBPK7jTjwKy56Hte9B+/HG0G3v8k5pWURExBYFSCJSYE4mpjB9UzwLd54mLdNi1x43FxMPhVYjonMIjaqWdXCHIiIihSioLQycA4nHjQHae+aCOY87kKZchLXvQtRH0HoEhD0F/jWd06+IiMhvKEASkbu2+9RVIqPiWHnggt2Dsct4uvFEWE1GdqhN1XIajC0iIqVIYB148H9wz+uwfSrsiISbV3Pfk5kC0Z/CtinQ9FFjTlLV5k5pV0REBBQgiUg+WSxWVh9OIDIqjh0n8njT+xvVynkxulMwA9sGUUaDsUVEpDTzqwjdX4dOL8DuObD1E7h2Kvc9VjPs/9p4hNxj3Lkt5B7Qpd8iIuJgCpBE5I6kZZr5ZtcZpkfFE3fZ/sHYTaqVZVyXEHo3q4q7qwZji4iIZPPwNeYctRkDh5YYd247vzfvfXHrjEeVZtDheWjSD1z14YyIiDiGAiQRsUticjqzo0/y5daTXEmxfzB2twYVGdclhPCQQA3GFhERyY2rGzQbAE37Q/xG2DwRjq/Je9+F/fDtWFjzNoQ/DS2Hgaef4/sVEZFSRQGSiOQq/nIK06LiWBRzhvQs+wZju7ua6BdanbGdQ2hQpYyDOxQRESlhTCYI6Wo8LhwwBm4fWASWrNz3XT8NK/8P1v8T2o41zmryq+ScnkVEpMRTgCQiNsWcvMLUjXH8dCgBq52Dsct6uTE0rBYjO9SmUlkvxzYoIiJSGlRpCo9OgR5/hejPIGYmZCTnviftGkR9aARPLQZBh2ehQj1ndCsiIiWYAiQRyWa2WFl16AJTN8ax69Q1u/fVKO/NmE7BPN4mCF9P/WdFRESkwJWrAfe/B11ehp1fwLbPITkh9z3mdNg1C3Z9CQ37GHduq9neOf2KiEiJo7/0RISbGWYWxZxm2qZ4Tiam2r2veY1yjOsSQq8mVXDTYGwRERHH8/aHzi8Zs472LTDOMrr8cx6brBC73HgEhRl3bqv/ALjod7eIiNhPAZJIKXYpKZ3ZW08wO/okV1Mz7d7Xs1ElIjqH0C44QIOxRURECoObJ7QaDqFD4eiPxsDtU1vz3nc6GuZHQ2A949K25gPBXZedi4hI3hQgiZRCxy4mM31THN/sOkuGnYOxPdxc6N+qOmM6BVO3kgZji4iIFAkuLtDgAeNxegdsmQiHlwN5DDBMPArLnoO1fzeGbbcdA97lndKyiIgUTwqQREoJq9XK9vgrREbFsfrwRbv3+fu4MyysFsPDa1OxjKcDOxQREZG7EtQWBs6By8dg6yewZ64xByk3KRdh7bsQ9RG0HgFhE8A/yDn9iohIsaIASaSEyzJb+PFgAlM3Hmfvmet276sZ4MPYzsEMaF0DHw/9p0JERKTYqFAXHvwf3PMabJ8K2yONO7PlJjMFoj+FbVOgaX9jTlKVZs7oVkREign9VShSQqWkZ/H1ztNM3xzP6Ss37d4XGuTP+C4h3NekCq4umm8kIiJSbPlVgu5vQKcXYfcc2PIJXD+V+x6rGfYvNB51uht3bgvpBpp5KCJS6ilAEilhLt5IY9bWE8yJPsX1m/YNxjaZ4N5GlRnXJYTWtcprMLaIiEhJ4uFrzDlqMwYOLTEGbl/Yl/e+42uNR5Vm0PEFaNwPXPXng4hIaaXfACIlxNGEJCKj4liy+xwZZvsGY3u6uTCgdQ3GdAompKKfgzsUERGRQuXqBs0GGJeoxW+AzZPg+Jq8913YD9+MgdVvQ/gEaDkMPPW+QUSktFGAJFKMWa1WtsYlErkxjnVHLtm9L8DX45fB2LUI9NNgbBERkVLFZDIuSwvpZoRDWz6G/YuMy9dyc/0UrPw/WP9PaDvWOKvJr5IzOhYRkSJAAZJIMZRltvD9gQtEboxj/1n7B2MHV/BlTKdg+reqgbeHqwM7FBERkWKhSjN4dCp0/ytEfwa7ZkFGcu570q5B1IdG8BQ6GMKfNQZ3i4hIiaYASaQYSU7PYsGO03yxKZ6z1+wfjN2mVnkiuoTQs1FlDcYWERGRP/IPgl7vQ9eXYecM2PY5JCfkvsecDjEzIWYWNOwDHZ+HoHZOaVdERJxPAZJIMXDhehozt5zgq20nSUrLsmuPyQS9mlRhbGdjMLaIiIhInrzLQ+eXIPxp2LfAOMvo8s95bLJC7HLjERRmBEn1e4GLy529dlYGHFkBx1bDub2QeBSy0sHNEwLrQbUWULcnNOgDbh75/hFFRCR/FCCJFGGxF24QuTGe7/aeJdNstWuPl7sLj7cJYnTHYGpX8HVwhyIiIlIiuXlCq+EQOhR+XglbJsGprXnvOx0N86OhQn0IfwaaDwR3r9z3mDNh62TjkXLxj+tZaZCw33jsngN+lSFsghFyubrn7+cTEZE7pgBJpIixWq1sPpbI1Kg4Nv5s/2DsCn4ejAivzdCwWpT31adyIiIiUgBcXKBhb+NxejtsngixK4A8Pti6/DMsew7WvWcM224z2ji76fcuHobF4+H83tu/b3KFwLrG3d7SkyHx2K0h38kJsPotOPgtPDIFKjUqkB9VRERypwBJpIjINFtYvu8cUzfGc/j8Dbv3hVT0JaJzCI+0rI6XuwZji4iIiIMEtYNBX8HlY7D1Y9gzz5iDlJvkBFjzDkR9BK1GQNhTxrwlgFPb4KsBkP7L+x6TCzTsC21GGZfCefjcOk5GqnF2084ZxqVyVosROk2/D4YsgprtHfMzi4hINgVIIoUsKS2T+dtP88XmeM5fT7N7X7vgAMZ1DqF7w0q4aDC2iIiIOEuFuvDgRLjnddg+FbZHGndmy01GMkRPNoZzN+0PjfrC0mduhUcVG0K/T6F6a9v7PXygTnfjcTYGlkyAS7HG/q8GwJhVUKlhgf6YIiJyOwVIIoXk3LWbzNxygnnbTpGUbt9gbBcTPNCsKhGdQwgN8ndsgyIiIiK58asE3d+Aji8Ys4m2Tobrp3LfYzXD/oXG41ch3WDQXPCwc3Zj9dYQsRbmPwFx640QafE4GLtGM5FERBxIAZKIkx08d51pUfEs23uOLIt9g7G93V0Z2DaIMZ2CCQrwyXuDiIiIiLN4+kHYk9B2LBxaYsxJurDPvr0VG95ZePQrD19jX2R340yk83uNAKvTC3favYiI2EkBkogTWK1WNh69TOTGODYdu2z3voplPBnZoTZD2tfE30eDsUVERKQIc3WDZgOMS9TiNxhB0vG1OdebXIzL1u40PPqVhy88/ClM72nMRIr+VHdmExFxIAVIIg6UkWXhu73nmBYVR+yFJLv31avkR0SXEB4OrYanmwZji4iISDFiMhmXpYV0gwv7YfMkOPDNrbuo/aph35xnHtmrRmto2AcOLzMGdscuhyaP3N0xRUTEJgVIIg5w/WYmc7edYuaWeBJu5HF3kt8IDwlkXJcQutavqMHYIiIiUvxVaQb9I6HHmxD9GWybAtZfZj+2GVUwr9FmtBEgARxbowBJRMRBFCCJFKAzV1OZsfkE87efIiXDnPcGwNXFRJ9fBmM3q1HOwR2KiIiIFAL/IOj1PsSthYuHweQKNcML5thBYcblcFYLnN9TMMcUEZE/UIAkUgAOnL3O1I1xrNh/HrOdg7F9PVwZ1K4mozrWpkZ5DcYWERGRUuBKvPFvYF1w9y6YY3r4GMe7/DNcPlYwxxQRkT9QgCSSTxaLlQ0/X2Lqxji2xiXava9yWU9GdQxmcLualPPWkEcREREpRbJ+ubTf069gj+vxy/Gy0gr2uCIikk0BksgdSs8ys3T3OSKj4jh6MdnufQ2rlCGicwgPtqiGh5uLAzsUERERKaLcPI2QJ93+91B2yfjleK7uYM4y7ggnIiIFSv9lFbHTtdQMvtp2iplbTnApyf7B2J3qVmBclxA616uAyaTB2CIiIlKKBdaDhP2QeAwybxbMZWwZqcbxAMwZ8FEjaNofmj8G1VoZd4UTEZG7pgBJJA+nr6QyfVM8C3eeJtXOwdhuLiYebFGNsZ2DaVJNg7FFREREAKjWwgiQrGY4tRXqdL/7Y56ONgZo/yrlImz7zHgE1oVmjxthUkDI3b+WiEgppgBJSiSLxUq/TzeTmmHGx8OVJRM64uJyZ58+7T19jalRcfyw/zx2zsXGz9ONJ9rXZGSH2lTzL6DBkCIiIiIlRd2esHuO8XznjIIJkHZ+kfNa4jFY/77xqNEWmg+EJo+Ab4W7f10RkVJGAZKUSLEXkth35nr210cSkmhUtWye+ywWK2tjLzI1Ko7t8Vfsfr2q5bwY3TGYge2CKOulwdgiIiIiNjXoA36VITkBYpfD2Rio3jr/xzsTA7Er7KzdYTxW/h/U6QHNH4cGvY27uImISJ4UIEmJFP27u6JFxyXmGiClZZpZvPsskVFxxF1Ksft1GlUty/guIfRpXhV3Vw3GFhEREcmVmweETYDVbxmXnS2ZABH/396dx0dV3f8ff2dnCUsWQgJhCwGChn1LlD2AgiuyaMEqivLtV1rbb6tt/VVFrf32YV26uSHFDVFEBBcEKxCWfiUBWUMghH0LIZAEhRAgBKdT7wAAQtRJREFU2/39ccnNDCSTSTKTmcTX8/G4D+6dnHvOSTzeZD5zzuckS4HNa15X0QXp80fsl685o6xE2v9v8wgMluJuNYNJXUaQfBsAHOAJiUapsgDSAzd2uaZc/oUifZB6VO+nHFFuQZHT9Y/o3kazhsfohq5hJMYGAACoicTZ0u6lUvZO6cxeadE06Z4PaxZEKrpg3ndmr3kd1VdKelpK/1Ta84VUdN7JegqktEXm0TxC6jVZ6jVFateP5NsAcBUCSGh0ysoMbbpq+dmmw/kqKzOsPEhHci9o/v8d1idbj+tSsXOfWgX4+eiOvu310LAuiousfjkcAAAAKuEXIE2cK80fJ10+Jx1aJ80bLd3xuhTtxHK2E1vNmUflwaOgVmZ9EXFSbJJ0y8tS5kpp1yfS/m/MGUfOuHBaSn3dPMK6mbOSek2RQq/9EBIAfowIIKHR2XvqvH64WGz32veFxcrMOa+LxaV6a/0h/XvPKRlOJsZu0cRf04d00owbOiuyVRM39BgAAOBHJqKnNH2JtHCyGUQ6s1eaP0aKu0Ua+KDUIcE+N1FRobnb2pa3zZxH5cvWglpJ0z8xg0flAppK8XeZR2G+tHuZlLbYvN9ZefultX8yj+jBZjDp+ruk5mGu+f4BoAEigIRGx3b5Wpfw5jqca+Y0mvX+Fh0/e9Hpetq3bqoHh3bR3YM6KDiI/1UAAABcquMQaeYqadksczmbUSZlfGkePr5SWKyZo6iowNxN7epcR1F9pIlv2QePrtYsVBo00zzOHjFnJaUtlnL3Od/PE5vN4+vfm7vI9ZpC8m0AP0q8K0aD88fle/RVWnaVX7edffSL0bH69eKdkuR08Ci+fUvNGt5VE+Ij5U9ibAAAAPeJiJMeWiOlvGYuHSvIMV83yqoO8gS3NRNxJ842l8M5K6SzNPxxadhjZsAqbbGUvqSizeqUlUj7vjaPwGCp520Vybd9/ZzvBwA0UASQ0KAYhqGv00/p1LlL1ZYNax6o2/u005++ylDeheoTZDcL9NP8+wcpISaUxNgAAAD1xS9AGvorMyC0d7l0YI2UvUPKPSCVXJL8m0jhsWai7Ngkc9e0mgSOrubjI7Xrax7j/igdXi+lfSJlfGHOdnJGUYG08yPzCG4rxU+Wek8x+8jfkQAaKQJIaFDOFhbrzPnL17zu5+ujNsFB1nVEyyC9MrWP/P189fF/JejXi3fq9LmK+84UXFZpmX0SpJJSQz0iWxA8AgAA8AS/AOn6ieZRX3z9pK6jzeOWl6V9K82ZSQdWO598uyBHSn3NPMK7S72mmsGkkM5u7ToA1DcCSGhQQpsHavWvR+jPKzO0Mv2U9XppmaF+HVvrifE91THMfj16bEQLffHzoZKkY3mF+t8VGfp69ym7MhN6ReqJ8T0V2jzQ/d8EAAAAvE9gMyl+knlcyJN2LzVzJh3f5Hwdufuktc+bR4chFcm3m4W6r98AUE8IIKHB6RjWTG/cO0Cph/L03Jd7tCf7nCRpZfoprck4rYUPD9Ggztf+kv7uSL6mz9ukotKKBIzXRbXU07ddp4QYdtQAAADAFc3DpMEPm0f+YWnXEintY3N3Nmcd32QeK38nxY41ZyV1H0/ybQANFgEkNFgJMWH68hdDtWTrcb3470zlFhSpqLRMizYfrzSA9NHmY1bwKDw4SI/f1F2TB3SQny9L1gAAAFCF0C7SiMel4Y+ZuZnSFpsBpQunnbu/rMRcGrdvpRTYwib59nCSbwNoUAggoUHz8/XR3YM6ytfHR48vSZMkBfhVHhAK8K3YUe13N/fQlIEd6qWPAAAAaAR8fKR2/cxj7JXk27s+kTK+rEHy7fPSzg/NIzhS6jVZ6jVFiupD8m0AXo89ytEofLYjyzqfPCBaJaVlenP9Qf168Q69uf6gSkrLNHlgtFXm8x0nPdFNAAAANAZ+/uaOcBPflB7bL02aL3W7SfKtwefzBaeklFelt0ZIrw2RNrwonT3iti4DQF0xAwkN3omzhdp4ME+S1Dmsmdq2bKK730rV1qNnrTKr9+Tor3f3VaewZjqaV6hvD+Yq6/uLat+6qae6DQAAgMYgsNmVmUSTpQu50u5lZr6kE985X0duppT8vHl0SLiSfHti3ZNvlxRJmV+Zu8qd3GnmcCq5LPkHSWHdpHZ9pNgxUo9bJH82kwHgGAEkNHjLtmXJMMzzlk0DNOHv/9H5y/bbrm45elYT/v4fdQ5vLkkyDGnZthP6+ehu9d1dAAAANFbNw22Sbx+ySb59wPk6jqeax8rfSd3GmkvceoyXAmrwwWdpsZTymnlUlqup5JKUs8s8tn8gBbeVEh6REmdLfgHOtwPgR4UlbGjQDMPQkm0nrOu0Ez9YwaMOoU31wqRe6hBq/rI9f7lEu7J+sMou2XpCRnnkCQAAAHCl0BhpxG+ln2+RHl4rDflvqXmE8/eXFUuZK6QlD0gvdpM+e0Q6tE4qK3V83+kM6V9J0uo59sEjHz8pvIfUfoD5r49NAu+CHLP8v5LM+wGgEsxAQoO25ehZHc0rvOb1u/q117N3XK8WTQI0oVeUnv58t5Ztz7IrcySvUFuPntXASnZsAwAAAFzCx0dq3988xj0vHV4npV1Jvl18wbk6is5LOxaaR4soKX6Sucwtsrd98u1jm6SFk6XL56607SvF3SoNfMBcGhfYzKbOQnOm05Z3pL3LJaNMyt4pzR8nTV8idRzish8BgMaBABIatCVbTthdtwjy1/MT43VH3/YVrzUJ0F/v7quRPdroyWXpdsvblmw9QQAJAAAA9cPP38w5FDtGKnpFylxpLnE7sEYyqplZVO58tpl8O+VVqU2cucSt1xSpuNA+eNQmTrrzdXPGUWUCm0ldR5tH1lZzhtOZveb9CydLM1dJEXGu+b4BNAosYUODdkffdgoPNhP+DewUohW/HGYXPLIv214rfjlMAzuFSJLCgwN1e5929dZXAAAAwBLY3Ey8Pf0T6TeZ0vgXpfYDa1bHmb1S8h+lv/eW5iVVBI9iRkoPJ1cdPLpa+wFm+ZiR5vXlc9KyWWYuJQC4ghlIaNBuiA3XV48O09fppzR9SEf5+zmOiXYIbaZFsxK0cNMx3RwfqbYtm9RTTwEAAIAqBLeRhswyj7yD0q5PpLTFUv5B5+soXw7XJk6650MzQFUTgc3N++aNNgNT2TvNJNxDf1WzegA0WsxAQoPXtmUT3X9D52qDR+X8/Xx1/w2dCR4BAADA+4R1lUb+XvrFVumhZGnIz6Rm4c7d6+NrLlurafCoXGBz6Y7XzXokKfV1ZiEBsBBAAgAAAABv4+MjRQ+Qxr9gLnGb/qnUa6oU0Kzqe+JudX7ZWlWiB0hxt5jnBTlmgm0AEAEkAAAAAPBufv5StzHSpHnSY/ulu+ZJsWMlHz/7cgMfcE17Ax+sOD+wxjV1AmjwCCABAAAAQEMRFCz1nirdu8ScmdQiynzdx0/qmOiaNjokVCxjy97hmjoBNHgEkAAAAACgIQpuI108a56HxUoBTV1Tb2Azsz5Jyj3gmjoBNHgEkAAAAACgoSq5bP4bFOzaegOv1FdyUVo6S9q5SDp/yrVtAGhQ/D3dAQAAAABALfkHSSWXpMsFrq23yKa+tI/NQ5Iirpe6jjKPjjeYs5UA/CgQQAIAAACAhiqsm5SzS8o7IBVfdM0ytqJCs77KnN5tHimvSn5BUscEqetoM6DUtpfkyyIXoLEigAQAAAAADVW7PmYAySiVjqWYwZy6Op4qGWXVlyu9LB1ebx6r50jNws1AUsyVGUot29W9LwC8BgEkAAAAAGioYsdI2z8wz7e845oA0pa3a3dfYa606xPzkKQ2cVdmJ42WOt0gBTave98AeAwBJAAAAABoqHrcIgW3lQpypL3LpaytUvsBta/vxFZp71fmeUBTKThKOnuodnWd2Wseqa9LfoFShyFX8ieNliL7sNwNaGAIIAEAAABAQ+UfKCU8Yi4hM8qkzx6RHk6u3WyfogvS549ULF8b8Xtp6K+ks0elQ2ulg8nSofXSpe9rXndpkXTkP+ax5jmpaagUM7Iif1Kr6JrXWVMlRVLmV9KB1dLJnVLefnMXO/8gM5dUuz7mjK4et5g/VwB2CCABAAAAQEOWOFvavVTK3mnO+Fk0Tbrnw5oFkYoumPed2WteR/WVEn9unod0kgbMMI+yUunkDulQsnRwrXR8k1RWUvM+X8w3+7x7qXkd3t0MJsWMkjoPlYKCa15nVUqLpZTXzOPC6Wu/XnLJzCOVs8tcDhjc1gzKJc6W/AJc1w+ggSOABAAAAAANmV+ANHGuNH+cdPmcdGidNG+0dMfrUrQTy9lObDVnHpUHj4JamfX5VfJ20dfPrDN6gDT8cenyeenIt1dmJ62VcvfV7nvI3Wcem96UfAOkDoMrlrtF9TXbrY3TGdKy/zKDa7Z8/KSwWDNQdbnA3HXOKDW/VpBjzujavdT8OUT0rF3bQCNDAAkAAAAAGrqIntL0JdLCyWYQ6cxeaf4YKe4WaeCDUocEKbBZRfmiQnO3tS1vmzmPypetBbWSpn8iRcQ5125QC6nHzeYhSd8fv7Lcba0ZyLqYX/PvpaxYOvqteSQ/LzVpfWW525WAUuuOztVzbFPFz0OSfHyluFulgQ84+Hm8Y+aSMsrMoNP8cebPteOQmn8fQCPjYxiG4elOwDslJiYqNTXV7rWEhASlpKR4qEcAAAAAHDq9V1o2q5IZN77mjJvAYKmofMZNmX2ZqD7SxLecDx5Vp6xMOrXTnJ10cK10LNUMDtVVaNeK3d06D5WatLy2zOmMihlZkrkj3J2vO5dgPGurmUvKmpHVUpq5ynU/F6AOPPk+nRlIAAAAANBYRMRJD60x8/2kvm4ux5LMYFFVy8vclfPH11dq1888hv3GzLN05NuKhNzlAZqayj9oHt/NM5eidRhs5k7qOtpsS4a5bK08eBQzsmY5odoPMBORL5pmzqK6fM4Myj20hpxI+FEjgAQAAAAAjYlfgLl7WuJscznWgTVS9g4p94CZMNq/iRQea+YWik0yl3XVR2AksLnUfZx5SNK5k1eWul1Z8laYW/M6jVLpWIp5rPtfqUkrqWU7cwaSZM48qmlC8fK+3vOhmUvqzF5zRlfKa+bPFfiRIoAEAAAAAI2RX4B0/UTz8EYt20n9pptHWZm5C9rBK7OTjqVIpUU1r/PSD+Yhmcv27ny95sGjcoHNzUTk88eYM7hSX2dnNvyoEUACAAAAAHiWr6+ZgymqjznLp6hQOrbxSkBprXR6d83rjLvVuZxHjkQPMBORZ3xpLgfcu9x7A3KAmxFAAgAAAAB4l8BmUuwY85Ck86fMfETlCbkvnK6+joEPuKYvAx80A0iSuRzQmwJIJUVS5lfSgdXSyZ1S3n6p5LLkHySFdZPa9TF/hj1ukfwDPd1bNHAEkAAAAAAA3q1FpNTnHvMwDClntxlMOrRWOrrRzO1ky8dP6pjomrY7JJjL4YwyM5eUNygtNnMypbxWeTCt5JK5JDBnl7T9A/clSsePCgEkAAAAAEDD4eMjRcabx42PSsUXpWOpZkBp4z8lGVJYrBTQ1DXtBTYz68vdZwauvn5CCu9uJuhu00NqFuqadpx1OsPcZS57p/3rPn5mP4OCpcsFUt4BM8m4ZC6/Wz1H2r1UmjhXiuhZv31Go0AAyQWKior08ccf66OPPtLu3buVk5OjkJAQdenSRXfddZdmzJih8PBwt7RdUFCgBQsWaPHixdq/f7/OnDmjNm3aqHv37po6daruvfdeBQcHu6VtAAAAAPC4gKZS11HmsfGf5mtBLn4PFHilvvJk2raat5HCe5jBpDY9KoJLLSLNYJcrHdskLZwsXT5nXvv4mrmeBj5gzpQKbFZRtqhQOp4qbXnHzN1klJlBp/njpOlLpI5DXNu36njLcjtv6UcD5GMYhuHpTjRke/fu1bRp07R9+/Yqy0REROidd97RhAkTXNp2SkqKpk+frsOHD1dZJiYmRh9++KGGDKn5wyExMVGpqal2ryUkJCglJaXGdQEAAACA2z3f1ly+Fd5D+vlm19X76iBzBlJNBLW8ElDqIbW5ElQK7y617mQmDa+p0xlm8Kc8eNQmztxlzplE4Vlbpc8ekc7srejbzFVSRFzN+1FT1S23u5q7ltt5Sz/qyJPv0wkg1cGJEyc0ZMgQnTx5UpLk4+Oj4cOHKzY2VqdPn9bq1at18eJFSVJAQIBWrlyppKQkl7SdlpamoUOH6vz581b9o0ePVnR0tI4fP67k5GSVlJRIklq2bKlvv/1W8fHxNWqDABIAAACABuWNoWbeHx8/6f9luWYZW1Gh9Of25gweV/BvKoXHXgksxVUEl0Jjqg5UlBZL/0qqWLYWM1K650MpsLnz7RZdkBZNM5ORS+aOdw+tcW9wpDbL7cpF9XHdcjtv6YcLePJ9OkvY6mD69OlW8KhTp0764osv1Lt3b+vrubm5uueee7RmzRoVFxdr6tSpOnjwoFq3bl2ndouLi3XXXXdZwaM+ffro888/V6dOnawyR44c0Z133qmdO3fq3LlzmjRpknbv3i1/f/6TAwAAAGik2vUxA0hGqXQsReo6uu51Hk91XfBIkkouSqd2mYctX38ptKsZULINLoV1kza/VRH8aBNX8+CRZJa/50Np3mhzJlL2TnM2ztBfueTbuoa3LLfzln40ArWYNwdJWrFihTZs2CBJCgwM1JdffmkXPJKk8PBwff7554qJiZEk5efn6y9/+Uud2543b54OHjwoSQoJCdHKlSvtgkeS1LlzZ61cuVIhISGSpH379untt9+uc9sAAAAA4LVix1Scb3nHNXVusXkfFdTSNXVWpqxEys2UMr6U/vOStPQhae5w6X+jpDXPmWV8fM1lazUNHpULbC7d8bpZj2Tmcyotdk3/bZ3OsA/atImTHlot3b3ADOrZBm0k87rraPPrD602y0vm/QsnS6f3Nux+NBIEkGrptddes87vv/9+9erVq9JyzZs313PPPWddz50711pa5oq2H3vsMUVFRVVaLioqSr/5zW8qvQ8AAAAAGp0et5i5ayRzBknW1rrVd2KrtPcr8zy4rfTbQ9JvD0sPfC3d9nczR07XJKlldN3aqU75sqq4W53LeeRI9AAp7hbzvCDH/Dm5UmmxuVysPGgTM1J6ONn5frcfYJaPGWleXz4nLZtV80CXt/SjESGAVAsFBQVas2aNdf3AAw84LD958mS1aNFCkjkLqXzmUm0cOHBAe/bssa5nzJjhsLzt19PS0qyZSwAAAADQ6PgHmkEdyVx+9NkjZu6f2ii6IH3+SMXytYRHzHxBzUKlTonSgBnSzX+WfrpU+vVu6Yks6eG10p1vSkP/R+oxwVyS5uPCt90DHb/3dL6eByvO966Qyly4RC/lNdcttyufAVS+3K4h9qMRISFOLWzcuFGXL1+WZM4wGjRokMPyQUFBSkhI0KpVqyR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      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 484,
       "width": 584
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[6, 5])\n",
    "plt.plot(Snwparr, relperm_nwp, '*-', label='Kr_nwp')\n",
    "plt.plot(Snwparr, relperm_wp, 'o-', label='Kr_wp')\n",
    "plt.xlabel('Snwp')\n",
    "plt.ylabel('Kr')\n",
    "plt.title('Relative Permeability in x direction')\n",
    "plt.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  },
  "vscode": {
   "interpreter": {
    "hash": "b58bd5559424689280ce24ff6229e536533c877108d283a6d2846312dfd182d7"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
